AMIE: A research AI system for diagnostic medical reasoning and conversations

AMIE: A research AI system for diagnostic medical reasoning and conversations

by Google AI

Posted by Alan Karthikesalingam and Vivek Natarajan, Research Leads, Google Research

The physician-patient conversation is a cornerstone of medicine, in which skilled and intentional communication drives diagnosis, management, empathy and trust. AI systems capable of such diagnostic dialogues could increase availability, accessibility, quality and consistency of care by being useful conversational partners to clinicians and patients alike. But approximating clinicians’ considerable expertise is a significant challenge.

Recent progress in large language models (LLMs) outside the medical domain has shown that they can plan, reason, and use relevant context to hold rich conversations. However, there are many aspects of good diagnostic dialogue that are unique to the medical domain. An effective clinician takes a complete “clinical history” and asks intelligent questions that help to derive a differential diagnosis. They wield considerable skill to foster an effective relationship, provide information clearly, make joint and informed decisions with the patient, respond empathically to their emotions, and support them in the next steps of care. While LLMs can accurately perform tasks such as medical summarization or answering medical questions, there has been little work specifically aimed towards developing these kinds of conversational diagnostic capabilities.

Inspired by this challenge, we developed Articulate Medical Intelligence Explorer (AMIE), a research AI system based on a LLM and optimized for diagnostic reasoning and conversations. We trained and evaluated AMIE along many dimensions that reflect quality in real-world clinical consultations from the perspective of both clinicians and patients. To scale AMIE across a multitude of disease conditions, specialties and scenarios, we developed a novel self-play based simulated diagnostic dialogue environment with automated feedback mechanisms to enrich and accelerate its learning process. We also introduced an inference time chain-of-reasoning strategy to improve AMIE’s diagnostic accuracy and conversation quality. Finally, we tested AMIE prospectively in real examples of multi-turn dialogue by simulating consultations with trained actors.

AMIE was optimized for diagnostic conversations, asking questions that help to reduce its uncertainty and improve diagnostic accuracy, while also balancing this with other requirements of effective clinical communication, such as empathy, fostering a relationship, and providing information clearly.

Evaluation of conversational diagnostic AI

Besides developing and optimizing AI systems themselves for diagnostic conversations, how to assess such systems is also an open question. Inspired by accepted tools used to measure consultation quality and clinical communication skills in real-world settings, we constructed a pilot evaluation rubric to assess diagnostic conversations along axes pertaining to history-taking, diagnostic accuracy, clinical management, clinical communication skills, relationship fostering and empathy.

We then designed a randomized, double-blind crossover study of text-based consultations with validated patient actors interacting either with board-certified primary care physicians (PCPs) or the AI system optimized for diagnostic dialogue. We set up our consultations in the style of an objective structured clinical examination (OSCE), a practical assessment commonly used in the real world to examine clinicians’ skills and competencies in a standardized and objective way. In a typical OSCE, clinicians might rotate through multiple stations, each simulating a real-life clinical scenario where they perform tasks such as conducting a consultation with a standardized patient actor (trained carefully to emulate a patient with a particular condition). Consultations were performed using a synchronous text-chat tool, mimicking the interface familiar to most consumers using LLMs today.

AMIE is a research AI system based on LLMs for diagnostic reasoning and dialogue.

AMIE: an LLM-based conversational diagnostic research AI system

We trained AMIE on real-world datasets comprising medical reasoning, medical summarization and real-world clinical conversations.

It is feasible to train LLMs using real-world dialogues developed by passively collecting and transcribing in-person clinical visits, however, two substantial challenges limit their effectiveness in training LLMs for medical conversations. First, existing real-world data often fails to capture the vast range of medical conditions and scenarios, hindering the scalability and comprehensiveness. Second, the data derived from real-world dialogue transcripts tends to be noisy, containing ambiguous language (including slang, jargon, humor and sarcasm), interruptions, ungrammatical utterances, and implicit references.

To address these limitations, we designed a self-play based simulated learning environment with automated feedback mechanisms for diagnostic medical dialogue in a virtual care setting, enabling us to scale AMIE’s knowledge and capabilities across many medical conditions and contexts. We used this environment to iteratively fine-tune AMIE with an evolving set of simulated dialogues in addition to the static corpus of real-world data described.

This process consisted of two self-play loops: (1) an “inner” self-play loop, where AMIE leveraged in-context critic feedback to refine its behavior on simulated conversations with an AI patient simulator; and (2) an “outer” self-play loop where the set of refined simulated dialogues were incorporated into subsequent fine-tuning iterations. The resulting new version of AMIE could then participate in the inner loop again, creating a virtuous continuous learning cycle.

Further, we also employed an inference time chain-of-reasoning strategy which enabled AMIE to progressively refine its response conditioned on the current conversation to arrive at an informed and grounded reply.

AMIE uses a novel self-play based simulated dialogue learning environment to improve the quality of diagnostic dialogue across a multitude of disease conditions, specialities and patient contexts.

We tested performance in consultations with simulated patients (played by trained actors), compared to those performed by 20 real PCPs using the randomized approach described above. AMIE and PCPs were assessed from the perspectives of both specialist attending physicians and our simulated patients in a randomized, blinded crossover study that included 149 case scenarios from OSCE providers in Canada, the UK and India in a diverse range of specialties and diseases.

Notably, our study was not designed to emulate either traditional in-person OSCE evaluations or the ways clinicians usually use text, email, chat or telemedicine. Instead, our experiment mirrored the most common way consumers interact with LLMs today, a potentially scalable and familiar mechanism for AI systems to engage in remote diagnostic dialogue.

Overview of the randomized study design to perform a virtual remote OSCE with simulated patients via online multi-turn synchronous text chat.

Performance of AMIE

In this setting, we observed that AMIE performed simulated diagnostic conversations at least as well as PCPs when both were evaluated along multiple clinically-meaningful axes of consultation quality. AMIE had greater diagnostic accuracy and superior performance for 28 of 32 axes from the perspective of specialist physicians, and 24 of 26 axes from the perspective of patient actors.

AMIE outperformed PCPs on multiple evaluation axes for diagnostic dialogue in our evaluations.
Specialist-rated top-k diagnostic accuracy. AMIE and PCPs top-k differential diagnosis (DDx) accuracy are compared across 149 scenarios with respect to the ground truth diagnosis (a) and all diagnoses listed within the accepted differential diagnoses (b). Bootstrapping (n=10,000) confirms all top-k differences between AMIE and PCP DDx accuracy are significant with p <0.05 after false discovery rate (FDR) correction.
Diagnostic conversation and reasoning qualities as assessed by specialist physicians. On 28 out of 32 axes, AMIE outperformed PCPs while being comparable on the rest.

Limitations

Our research has several limitations and should be interpreted with appropriate caution. Firstly, our evaluation technique likely underestimates the real-world value of human conversations, as the clinicians in our study were limited to an unfamiliar text-chat interface, which permits large-scale LLM–patient interactions but is not representative of usual clinical practice. Secondly, any research of this type must be seen as only a first exploratory step on a long journey. Transitioning from a LLM research prototype that we evaluated in this study to a safe and robust tool that could be used by people and those who provide care for them will require significant additional research. There are many important limitations to be addressed, including experimental performance under real-world constraints and dedicated exploration of such important topics as health equity and fairness, privacy, robustness, and many more, to ensure the safety and reliability of the technology.

AMIE as an aid to clinicians

In a recently released preprint, we evaluated the ability of an earlier iteration of the AMIE system to generate a DDx alone or as an aid to clinicians. Twenty (20) generalist clinicians evaluated 303 challenging, real-world medical cases sourced from the New England Journal of Medicine (NEJM) ClinicoPathologic Conferences (CPCs). Each case report was read by two clinicians randomized to one of two assistive conditions: either assistance from search engines and standard medical resources, or AMIE assistance in addition to these tools. All clinicians provided a baseline, unassisted DDx prior to using the respective assistive tools.

Assisted randomized reader study setup to investigate the assistive effect of AMIE to clinicians in solving complex diagnostic case challenges from the New England Journal of Medicine.

AMIE exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% vs. 33.6%, p= 0.04). Comparing the two assisted study arms, the top-10 accuracy was higher for clinicians assisted by AMIE, compared to clinicians without AMIE assistance (24.6%, p<0.01) and clinicians with search (5.45%, p=0.02). Further, clinicians assisted by AMIE arrived at more comprehensive differential lists than those without AMIE assistance.

In addition to strong standalone performance, using the AMIE system led to significant assistive effect and improvements in diagnostic accuracy of the clinicians in solving these complex case challenges.

It’s worth noting that NEJM CPCs are not representative of everyday clinical practice. They are unusual case reports in only a few hundred individuals so offer limited scope for probing important issues like equity or fairness.

Bold and responsible research in healthcare — the art of the possible

Access to clinical expertise remains scarce around the world. While AI has shown great promise in specific clinical applications, engagement in the dynamic, conversational diagnostic journeys of clinical practice requires many capabilities not yet demonstrated by AI systems. Doctors wield not only knowledge and skill but a dedication to myriad principles, including safety and quality, communication, partnership and teamwork, trust, and professionalism. Realizing these attributes in AI systems is an inspiring challenge that should be approached responsibly and with care. AMIE is our exploration of the “art of the possible”, a research-only system for safely exploring a vision of the future where AI systems might be better aligned with attributes of the skilled clinicians entrusted with our care. It is early experimental-only work, not a product, and has several limitations that we believe merit rigorous and extensive further scientific studies in order to envision a future in which conversational, empathic and diagnostic AI systems might become safe, helpful and accessible.

Acknowledgements

The research described here is joint work across many teams at Google Research and Google Deepmind. We are grateful to all our co-authors – Tao Tu, Mike Schaekermann, Anil Palepu, Daniel McDuff, Jake Sunshine, Khaled Saab, Jan Freyberg, Ryutaro Tanno, Amy Wang, Brenna Li, Mohamed Amin, Sara Mahdavi, Karan Sighal, Shekoofeh Azizi, Nenad Tomasev, Yun Liu, Yong Cheng, Le Hou, Albert Webson, Jake Garrison, Yash Sharma, Anupam Pathak, Sushant Prakash, Philip Mansfield, Shwetak Patel, Bradley Green, Ewa Dominowska, Renee Wong, Juraj Gottweis, Dale Webster, Katherine Chou, Christopher Semturs, Joelle Barral, Greg Corrado and Yossi Matias. We also thank Sami Lachgar, Lauren Winer and John Guilyard for their support with narratives and the visuals. Finally, we are grateful to Michael Howell, James Maynika, Jeff Dean, Karen DeSalvo, Zoubin Gharahmani and Demis Hassabis for their support during the course of this project.

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AMIE: A research AI system for diagnostic medical reasoning and conversations

AMIE: A research AI system for diagnostic medical reasoning and conversations

by Google AI

Posted by Alan Karthikesalingam and Vivek Natarajan, Research Leads, Google Research

The physician-patient conversation is a cornerstone of medicine, in which skilled and intentional communication drives diagnosis, management, empathy and trust. AI systems capable of such diagnostic dialogues could increase availability, accessibility, quality and consistency of care by being useful conversational partners to clinicians and patients alike. But approximating clinicians’ considerable expertise is a significant challenge.

Recent progress in large language models (LLMs) outside the medical domain has shown that they can plan, reason, and use relevant context to hold rich conversations. However, there are many aspects of good diagnostic dialogue that are unique to the medical domain. An effective clinician takes a complete “clinical history” and asks intelligent questions that help to derive a differential diagnosis. They wield considerable skill to foster an effective relationship, provide information clearly, make joint and informed decisions with the patient, respond empathically to their emotions, and support them in the next steps of care. While LLMs can accurately perform tasks such as medical summarization or answering medical questions, there has been little work specifically aimed towards developing these kinds of conversational diagnostic capabilities.

Inspired by this challenge, we developed Articulate Medical Intelligence Explorer (AMIE), a research AI system based on a LLM and optimized for diagnostic reasoning and conversations. We trained and evaluated AMIE along many dimensions that reflect quality in real-world clinical consultations from the perspective of both clinicians and patients. To scale AMIE across a multitude of disease conditions, specialties and scenarios, we developed a novel self-play based simulated diagnostic dialogue environment with automated feedback mechanisms to enrich and accelerate its learning process. We also introduced an inference time chain-of-reasoning strategy to improve AMIE’s diagnostic accuracy and conversation quality. Finally, we tested AMIE prospectively in real examples of multi-turn dialogue by simulating consultations with trained actors.

AMIE was optimized for diagnostic conversations, asking questions that help to reduce its uncertainty and improve diagnostic accuracy, while also balancing this with other requirements of effective clinical communication, such as empathy, fostering a relationship, and providing information clearly.

Evaluation of conversational diagnostic AI

Besides developing and optimizing AI systems themselves for diagnostic conversations, how to assess such systems is also an open question. Inspired by accepted tools used to measure consultation quality and clinical communication skills in real-world settings, we constructed a pilot evaluation rubric to assess diagnostic conversations along axes pertaining to history-taking, diagnostic accuracy, clinical management, clinical communication skills, relationship fostering and empathy.

We then designed a randomized, double-blind crossover study of text-based consultations with validated patient actors interacting either with board-certified primary care physicians (PCPs) or the AI system optimized for diagnostic dialogue. We set up our consultations in the style of an objective structured clinical examination (OSCE), a practical assessment commonly used in the real world to examine clinicians’ skills and competencies in a standardized and objective way. In a typical OSCE, clinicians might rotate through multiple stations, each simulating a real-life clinical scenario where they perform tasks such as conducting a consultation with a standardized patient actor (trained carefully to emulate a patient with a particular condition). Consultations were performed using a synchronous text-chat tool, mimicking the interface familiar to most consumers using LLMs today.

AMIE is a research AI system based on LLMs for diagnostic reasoning and dialogue.

AMIE: an LLM-based conversational diagnostic research AI system

We trained AMIE on real-world datasets comprising medical reasoning, medical summarization and real-world clinical conversations.

It is feasible to train LLMs using real-world dialogues developed by passively collecting and transcribing in-person clinical visits, however, two substantial challenges limit their effectiveness in training LLMs for medical conversations. First, existing real-world data often fails to capture the vast range of medical conditions and scenarios, hindering the scalability and comprehensiveness. Second, the data derived from real-world dialogue transcripts tends to be noisy, containing ambiguous language (including slang, jargon, humor and sarcasm), interruptions, ungrammatical utterances, and implicit references.

To address these limitations, we designed a self-play based simulated learning environment with automated feedback mechanisms for diagnostic medical dialogue in a virtual care setting, enabling us to scale AMIE’s knowledge and capabilities across many medical conditions and contexts. We used this environment to iteratively fine-tune AMIE with an evolving set of simulated dialogues in addition to the static corpus of real-world data described.

This process consisted of two self-play loops: (1) an “inner” self-play loop, where AMIE leveraged in-context critic feedback to refine its behavior on simulated conversations with an AI patient simulator; and (2) an “outer” self-play loop where the set of refined simulated dialogues were incorporated into subsequent fine-tuning iterations. The resulting new version of AMIE could then participate in the inner loop again, creating a virtuous continuous learning cycle.

Further, we also employed an inference time chain-of-reasoning strategy which enabled AMIE to progressively refine its response conditioned on the current conversation to arrive at an informed and grounded reply.

AMIE uses a novel self-play based simulated dialogue learning environment to improve the quality of diagnostic dialogue across a multitude of disease conditions, specialities and patient contexts.

We tested performance in consultations with simulated patients (played by trained actors), compared to those performed by 20 real PCPs using the randomized approach described above. AMIE and PCPs were assessed from the perspectives of both specialist attending physicians and our simulated patients in a randomized, blinded crossover study that included 149 case scenarios from OSCE providers in Canada, the UK and India in a diverse range of specialties and diseases.

Notably, our study was not designed to emulate either traditional in-person OSCE evaluations or the ways clinicians usually use text, email, chat or telemedicine. Instead, our experiment mirrored the most common way consumers interact with LLMs today, a potentially scalable and familiar mechanism for AI systems to engage in remote diagnostic dialogue.

Overview of the randomized study design to perform a virtual remote OSCE with simulated patients via online multi-turn synchronous text chat.

Performance of AMIE

In this setting, we observed that AMIE performed simulated diagnostic conversations at least as well as PCPs when both were evaluated along multiple clinically-meaningful axes of consultation quality. AMIE had greater diagnostic accuracy and superior performance for 28 of 32 axes from the perspective of specialist physicians, and 24 of 26 axes from the perspective of patient actors.

AMIE outperformed PCPs on multiple evaluation axes for diagnostic dialogue in our evaluations.
Specialist-rated top-k diagnostic accuracy. AMIE and PCPs top-k differential diagnosis (DDx) accuracy are compared across 149 scenarios with respect to the ground truth diagnosis (a) and all diagnoses listed within the accepted differential diagnoses (b). Bootstrapping (n=10,000) confirms all top-k differences between AMIE and PCP DDx accuracy are significant with p <0.05 after false discovery rate (FDR) correction.
Diagnostic conversation and reasoning qualities as assessed by specialist physicians. On 28 out of 32 axes, AMIE outperformed PCPs while being comparable on the rest.

Limitations

Our research has several limitations and should be interpreted with appropriate caution. Firstly, our evaluation technique likely underestimates the real-world value of human conversations, as the clinicians in our study were limited to an unfamiliar text-chat interface, which permits large-scale LLM–patient interactions but is not representative of usual clinical practice. Secondly, any research of this type must be seen as only a first exploratory step on a long journey. Transitioning from a LLM research prototype that we evaluated in this study to a safe and robust tool that could be used by people and those who provide care for them will require significant additional research. There are many important limitations to be addressed, including experimental performance under real-world constraints and dedicated exploration of such important topics as health equity and fairness, privacy, robustness, and many more, to ensure the safety and reliability of the technology.

AMIE as an aid to clinicians

In a recently released preprint, we evaluated the ability of an earlier iteration of the AMIE system to generate a DDx alone or as an aid to clinicians. Twenty (20) generalist clinicians evaluated 303 challenging, real-world medical cases sourced from the New England Journal of Medicine (NEJM) ClinicoPathologic Conferences (CPCs). Each case report was read by two clinicians randomized to one of two assistive conditions: either assistance from search engines and standard medical resources, or AMIE assistance in addition to these tools. All clinicians provided a baseline, unassisted DDx prior to using the respective assistive tools.

Assisted randomized reader study setup to investigate the assistive effect of AMIE to clinicians in solving complex diagnostic case challenges from the New England Journal of Medicine.

AMIE exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% vs. 33.6%, p= 0.04). Comparing the two assisted study arms, the top-10 accuracy was higher for clinicians assisted by AMIE, compared to clinicians without AMIE assistance (24.6%, p<0.01) and clinicians with search (5.45%, p=0.02). Further, clinicians assisted by AMIE arrived at more comprehensive differential lists than those without AMIE assistance.

In addition to strong standalone performance, using the AMIE system led to significant assistive effect and improvements in diagnostic accuracy of the clinicians in solving these complex case challenges.

It’s worth noting that NEJM CPCs are not representative of everyday clinical practice. They are unusual case reports in only a few hundred individuals so offer limited scope for probing important issues like equity or fairness.

Bold and responsible research in healthcare — the art of the possible

Access to clinical expertise remains scarce around the world. While AI has shown great promise in specific clinical applications, engagement in the dynamic, conversational diagnostic journeys of clinical practice requires many capabilities not yet demonstrated by AI systems. Doctors wield not only knowledge and skill but a dedication to myriad principles, including safety and quality, communication, partnership and teamwork, trust, and professionalism. Realizing these attributes in AI systems is an inspiring challenge that should be approached responsibly and with care. AMIE is our exploration of the “art of the possible”, a research-only system for safely exploring a vision of the future where AI systems might be better aligned with attributes of the skilled clinicians entrusted with our care. It is early experimental-only work, not a product, and has several limitations that we believe merit rigorous and extensive further scientific studies in order to envision a future in which conversational, empathic and diagnostic AI systems might become safe, helpful and accessible.

Acknowledgements

The research described here is joint work across many teams at Google Research and Google Deepmind. We are grateful to all our co-authors – Tao Tu, Mike Schaekermann, Anil Palepu, Daniel McDuff, Jake Sunshine, Khaled Saab, Jan Freyberg, Ryutaro Tanno, Amy Wang, Brenna Li, Mohamed Amin, Sara Mahdavi, Karan Sighal, Shekoofeh Azizi, Nenad Tomasev, Yun Liu, Yong Cheng, Le Hou, Albert Webson, Jake Garrison, Yash Sharma, Anupam Pathak, Sushant Prakash, Philip Mansfield, Shwetak Patel, Bradley Green, Ewa Dominowska, Renee Wong, Juraj Gottweis, Dale Webster, Katherine Chou, Christopher Semturs, Joelle Barral, Greg Corrado and Yossi Matias. We also thank Sami Lachgar, Lauren Winer and John Guilyard for their support with narratives and the visuals. Finally, we are grateful to Michael Howell, James Maynika, Jeff Dean, Karen DeSalvo, Zoubin Gharahmani and Demis Hassabis for their support during the course of this project.

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Can large language models identify and correct their mistakes?

Can large language models identify and correct their mistakes?

by Google AI

Posted by Gladys Tyen, Intern, Google Research

LLMs are increasingly popular for reasoning tasks, such as multi-turn QA, task completion, code generation, or mathematics. Yet much like people, they do not always solve problems correctly on the first try, especially on tasks for which they were not trained. Therefore, for such systems to be most useful, they should be able to 1) identify where their reasoning went wrong and 2) backtrack to find another solution.

This has led to a surge in methods related to self-correction, where an LLM is used to identify problems in its own output, and then produce improved results based on the feedback. Self-correction is generally thought of as a single process, but we decided to break it down into two components, mistake finding and output correction.

In “LLMs cannot find reasoning errors, but can correct them!”, we test state-of-the-art LLMs on mistake finding and output correction separately. We present BIG-Bench Mistake, an evaluation benchmark dataset for mistake identification, which we use to address the following questions:

  1. Can LLMs find logical mistakes in Chain-of-Thought (CoT) style reasoning?
  2. Can mistake-finding be used as a proxy for correctness?
  3. Knowing where the mistake is, can LLMs then be prompted to backtrack and arrive at the correct answer?
  4. Can mistake finding as a skill generalize to tasks the LLMs have never seen?

About our dataset

Mistake finding is an underexplored problem in natural language processing, with a particular lack of evaluation tasks in this domain. To best assess the ability of LLMs to find mistakes, evaluation tasks should exhibit mistakes that are non-ambiguous. To our knowledge, most current mistake-finding datasets do not go beyond the realm of mathematics for this reason.

To assess the ability of LLMs to reason about mistakes outside of the math domain, we produce a new dataset for use by the research community, called BIG-Bench Mistake. This dataset consists of Chain-of-Thought traces generated using PaLM 2 on five tasks in BIG-Bench. Each trace is annotated with the location of the first logical mistake.

To maximize the number of mistakes in our dataset, we sample 255 traces where the answer is incorrect (so we know there is definitely a mistake), and 45 traces where the answer is correct (so there may or may not be a mistake). We then ask human labelers to go through each trace and identify the first mistake step. Each trace has been annotated by at least three labelers, whose answers had inter-rater reliability levels of >0.98 (using Krippendorff’s α). The labeling was done for all tasks except the Dyck Languages task, which involves predicting the sequence of closing parentheses for a given input sequence. This task we labeled algorithmically.

The logical errors made in this dataset are simple and unambiguous, providing a good benchmark for testing an LLM’s ability to find its own mistakes before using them on harder, more ambiguous tasks.

Core questions about mistake identification

1. Can LLMs find logical mistakes in Chain-of-Thought style reasoning?

First, we want to find out if LLMs can identify mistakes independently of their ability to correct them. We attempt multiple prompting methods to test GPT series models for their ability to locate mistakes (prompts here) under the assumption that they are generally representative of modern LLM performance.

Generally, we found these state-of-the-art models perform poorly, with the best model achieving 52.9% accuracy overall. Hence, there is a need to improve LLMs’ ability in this area of reasoning.

In our experiments, we try three different prompting methods: direct (trace), direct (step) and CoT (step). In direct (trace), we provide the LLM with the trace and ask for the location step of the mistake or no mistake. In direct (step), we prompt the LLM to ask itself this question for each step it takes. In CoT (step), we prompt the LLM to give its reasoning for whether each step is a mistake or not a mistake.

A diagram showing the three prompting methods direct (trace), direct (step) and CoT (step).

Our finding is in line and builds upon prior results, but goes further in showing that LLMs struggle with even simple and unambiguous mistakes (for comparison, our human raters without prior expertise solve the problem with a high degree of agreement). We hypothesize that this is a big reason why LLMs are unable to self-correct reasoning errors. See the paper for the full results.

2. Can mistake-finding be used as a proxy for correctness of the answer?

When people are confronted with a problem where we are unsure of the answer, we can work through our solutions step-by-step. If no error is found, we can make the assumption that we did the right thing.

While we hypothesized that this would work similarly for LLMs, we discovered that this is a poor strategy. On our dataset of 85% incorrect traces and 15% correct traces, using this method is not much better than the naïve strategy of always labeling traces as incorrect, which gives a weighted average F1 of 78.

A diagram showing how well mistake-finding with LLMs can be used as a proxy for correctness of the answer on each dataset.

3. Can LLMs backtrack knowing where the error is?

Since we’ve shown that LLMs exhibit poor performance in finding reasoning errors in CoT traces, we want to know whether LLMs can even correct errors at all, even if they know where the error is.

Note that knowing the mistake location is different from knowing the right answer: CoT traces can contain logical mistakes even if the final answer is correct, or vice versa. In most real-world situations, we won’t know what the right answer is, but we might be able to identify logical errors in intermediate steps.

We propose the following backtracking method:

  1. Generate CoT traces as usual, at temperature = 0. (Temperature is a parameter that controls the randomness of generated responses, with higher values producing more diverse and creative outputs, usually at the expense of quality.)
  2. Identify the location of the first logical mistake (for example with a classifier, or here we just use labels from our dataset).
  3. Re-generate the mistake step at temperature = 1 and produce a set of eight outputs. Since the original output is known to lead to incorrect results, the goal is to find an alternative generation at this step that is significantly different from the original.
  4. From these eight outputs, select one that is different from the original mistake step. (We just use exact matching here, but in the future this can be something more sophisticated.)
  5. Using the new step, generate the rest of the trace as normal at temperature = 0.

It’s a very simple method that does not require any additional prompt crafting and avoids having to re-generate the entire trace. We test it using the mistake location data from BIG-Bench Mistake, and we find that it can correct CoT errors.

Recent work showed that self-correction methods, like Reflexion and RCI, cause deterioration in accuracy scores because there are more correct answers becoming incorrect than vice versa. Our method, on the other hand, produces more gains (by correcting wrong answers) than losses (by changing right answers to wrong answers).

We also compare our method with a random baseline, where we randomly assume a step to be a mistake. Our results show that this random baseline does produce some gains, but not as much as backtracking with the correct mistake location, and with more losses.

A diagram showing the gains and losses in accuracy for our method as well as a random baseline on each dataset.

4. Can mistake finding generalize to tasks the LLMs have never seen?

To answer this question, we fine-tuned a small model on four of the BIG-Bench tasks and tested it on the fifth, held-out task. We do this for every task, producing five fine-tuned models in total. Then we compare the results with just zero-shot prompting PaLM 2-L-Unicorn, a much larger model.

Bar chart showing the accuracy improvement of the fine-tuned small model compared to zero-shot prompting with PaLM 2-L-Unicorn.

Our results show that the much smaller fine-tuned reward model generally performs better than zero-shot prompting a large model, even though the reward model has never seen data from the task in the test set. The only exception is logical deduction, where it performs on par with zero-shot prompting.

This is a very promising result as we can potentially just use a small fine-tuned reward model to perform backtracking and improve accuracy on any task, even if we don’t have the data for it. This smaller reward model is completely independent of the generator LLM, and can be updated and further fine-tuned for individual use cases.

An illustration showing how our backtracking method works.

Conclusion

In this work, we created an evaluation benchmark dataset that the wider academic community can use to evaluate future LLMs. We further showed that LLMs currently struggle to find logical errors. However, if they could, we show the effectiveness of backtracking as a strategy that can provide gains on tasks. Finally, a smaller reward model can be trained on general mistake-finding tasks and be used to improve out-of-domain mistake finding, showing that mistake-finding can generalize.

Acknowledgements

Thank you to Peter Chen, Tony Mak, Hassan Mansoor and Victor Cărbune for contributing ideas and helping with the experiments and data collection. We would also like to thank Sian Gooding and Vicky Zayats for their comments and suggestions on the paper.

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Responsible AI at Google Research: User Experience Team

Responsible AI at Google Research: User Experience Team

by Google AI

Posted by Ayça Çakmakli, UX Lead, Google Research, Responsible AI and Human Centered Technology Team

Google’s Responsible AI User Experience (Responsible AI UX) team is a product-minded team embedded within Google Research. This unique positioning requires us to apply responsible AI development practices to our user-centered user experience (UX) design process. In this post, we describe the importance of UX design and responsible AI in product development, and share a few examples of how our team’s capabilities and cross-functional collaborations have led to responsible development across Google.

First, the UX part. We are a multi-disciplinary team of product design experts: designers, engineers, researchers, and strategists who manage the user-centered UX design process from early-phase ideation and problem framing to later-phase user-interface (UI) design, prototyping and refinement. We believe that effective product development occurs when there is clear alignment between significant unmet user needs and a product’s primary value proposition, and that this alignment is reliably achieved via a thorough user-centered UX design process.

And second, recognizing generative AI’s (GenAI) potential to significantly impact society, we embrace our role as the primary user advocate as we continue to evolve our UX design process to meet the unique challenges AI poses, maximizing the benefits and minimizing the risks. As we navigate through each stage of an AI-powered product design process, we place a heightened emphasis on the ethical, societal, and long-term impact of our decisions. We contribute to the ongoing development of comprehensive safety and inclusivity protocols that define design and deployment guardrails around key issues like content curation, security, privacy, model capabilities, model access, equitability, and fairness that help mitigate GenAI risks.

Responsible AI UX is constantly evolving its user-centered product design process to meet the needs of a GenAI-powered product landscape with greater sensitivity to the needs of users and society and an emphasis on ethical, societal, and long-term impact.

Responsibility in product design is also reflected in the user and societal problems we choose to address and the programs we resource. Thus, we encourage the prioritization of user problems with significant scale and severity to help maximize the positive impact of GenAI technology.

Communication across teams and disciplines is essential to responsible product design. The seamless flow of information and insight from user research teams to product design and engineering teams, and vice versa, is essential to good product development. One of our team’s core objectives is to ensure the practical application of deep user-insight into AI-powered product design decisions at Google by bridging the communication gap between the vast technological expertise of our engineers and the user/societal expertise of our academics, research scientists, and user-centered design research experts. We’ve built a multidisciplinary team with expertise in these areas, deepening our empathy for the communication needs of our audience, and enabling us to better interface between our user & society experts and our technical experts. We create frameworks, guidebooks, prototypes, cheatsheets, and multimedia tools to help bring insights to life for the right people at the right time.

Facilitating responsible GenAI prototyping and development

During collaborations between Responsible AI UX, the People + AI Research (PAIR) initiative and Labs, we identified that prototyping can afford a creative opportunity to engage with large language models (LLM), and is often the first step in GenAI product development. To address the need to introduce LLMs into the prototyping process, we explored a range of different prompting designs. Then, we went out into the field, employing various external, first-person UX design research methodologies to draw out insight and gain empathy for the user’s perspective. Through user/designer co-creation sessions, iteration, and prototyping, we were able to bring internal stakeholders, product managers, engineers, writers, sales, and marketing teams along to ensure that the user point of view was well understood and to reinforce alignment across teams.

The result of this work was MakerSuite, a generative AI platform launched at Google I/O 2023 that enables people, even those without any ML experience, to prototype creatively using LLMs. The team’s first-hand experience with users and understanding of the challenges they face allowed us to incorporate our AI Principles into the MakerSuite product design. Product features like safety filters, for example, enable users to manage outcomes, leading to easier and more responsible product development with MakerSuite.

Because of our close collaboration with product teams, we were able to adapt text-only prototyping to support multimodal interaction with Google AI Studio, an evolution of MakerSuite. Now, Google AI Studio enables developers and non-developers alike to seamlessly leverage Google’s latest Gemini model to merge multiple modality inputs, like text and image, in product explorations. Facilitating product development in this way provides us with the opportunity to better use AI to identify appropriateness of outcomes and unlocks opportunities for developers and non-developers to play with AI sandboxes. Together with our partners, we continue to actively push this effort in the products we support.

Google AI studio enables developers and non-developers to leverage Google Cloud infrastructure and merge multiple modality inputs in their product explorations.

Equitable speech recognition

Multiple external studies, as well as Google’s own research, have identified an unfortunate deficiency in the ability of current speech recognition technology to understand Black speakers on average, relative to White speakers. As multimodal AI tools begin to rely more heavily on speech prompts, this problem will grow and continue to alienate users. To address this problem, the Responsible AI UX team is partnering with world-renowned linguists and scientists at Howard University, a prominent HBCU, to build a high quality African-American English dataset to improve the design of our speech technology products to make them more accessible. Called Project Elevate Black Voices, this effort will allow Howard University to share the dataset with those looking to improve speech technology while establishing a framework for responsible data collection, ensuring the data benefits Black communities. Howard University will retain the ownership and licensing of the dataset and serve as stewards for its responsible use. At Google, we’re providing funding support and collaborating closely with our partners at Howard University to ensure the success of this program.

Equitable computer vision

The Gender Shades project highlighted that computer vision systems struggle to detect people with darker skin tones, and performed particularly poorly for women with darker skin tones. This is largely due to the fact that the datasets used to train these models were not inclusive to a wide range of skin tones. To address this limitation, the Responsible AI UX team has been partnering with sociologist Dr. Ellis Monk to release the Monk Skin Tone Scale (MST), a skin tone scale designed to be more inclusive of the spectrum of skin tones around the world. It provides a tool to assess the inclusivity of datasets and model performance across an inclusive range of skin tones, resulting in features and products that work better for everyone.

We have integrated MST into a range of Google products, such as Search, Google Photos, and others. We also open sourced MST, published our research, described our annotation practices, and shared an example dataset to encourage others to easily integrate it into their products. The Responsible AI UX team continues to collaborate with Dr. Monk, utilizing the MST across multiple product applications and continuing to do international research to ensure that it is globally inclusive.

Consulting & guidance

As teams across Google continue to develop products that leverage the capabilities of GenAI models, our team recognizes that the challenges they face are varied and that market competition is significant. To support teams, we develop actionable assets to facilitate a more streamlined and responsible product design process that considers available resources. We act as a product-focused design consultancy, identifying ways to scale services, share expertise, and apply our design principles more broadley. Our goal is to help all product teams at Google connect significant unmet user needs with technology benefits via great responsible product design.

One way we have been doing this is with the creation of the People + AI Guidebook, an evolving summative resource of many of the responsible design lessons we’ve learned and recommendations we’ve made for internal and external stakeholders. With its forthcoming, rolling updates focusing specifically on how to best design and consider user needs with GenAI, we hope that our internal teams, external stakeholders, and larger community will have useful and actionable guidance at the most critical milestones in the product development journey.

The People + AI Guidebook has six chapters, designed to cover different aspects of the product life cycle.

If you are interested in reading more about Responsible AI UX and how we are specifically thinking about designing responsibly with Generative AI, please check out this Q&A piece.

Acknowledgements

Shout out to our the Responsible AI UX team members: Aaron Donsbach, Alejandra Molina, Courtney Heldreth, Diana Akrong, Ellis Monk, Femi Olanubi, Hope Neveux, Kafayat Abdul, Key Lee, Mahima Pushkarna, Sally Limb, Sarah Post, Sures Kumar Thoddu Srinivasan, Tesh Goyal, Ursula Lauriston, and Zion Mengesha. Special thanks to Michelle Cohn for her contributions to this work.

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