Introducing ASPIRE for selective prediction in LLMs

Introducing ASPIRE for selective prediction in LLMs

by Google AI

Posted by Jiefeng Chen, Student Researcher, and Jinsung Yoon, Research Scientist, Cloud AI Team

In the fast-evolving landscape of artificial intelligence, large language models (LLMs) have revolutionized the way we interact with machines, pushing the boundaries of natural language understanding and generation to unprecedented heights. Yet, the leap into high-stakes decision-making applications remains a chasm too wide, primarily due to the inherent uncertainty of model predictions. Traditional LLMs generate responses recursively, yet they lack an intrinsic mechanism to assign a confidence score to these responses. Although one can derive a confidence score by summing up the probabilities of individual tokens in the sequence, traditional approaches typically fall short in reliably distinguishing between correct and incorrect answers. But what if LLMs could gauge their own confidence and only make predictions when they’re sure?

Selective prediction aims to do this by enabling LLMs to output an answer along with a selection score, which indicates the probability that the answer is correct. With selective prediction, one can better understand the reliability of LLMs deployed in a variety of applications. Prior research, such as semantic uncertainty and self-evaluation, has attempted to enable selective prediction in LLMs. A typical approach is to use heuristic prompts like “Is the proposed answer True or False?” to trigger self-evaluation in LLMs. However, this approach may not work well on challenging question answering (QA) tasks.

The OPT-2.7B model incorrectly answers a question from the TriviaQA dataset: “Which vitamin helps regulate blood clotting?” with “Vitamin C”. Without selective prediction, LLMs may output the wrong answer which, in this case, could lead users to take the wrong vitamin. With selective prediction, LLMs will output an answer along with a selection score. If the selection score is low (0.1), LLMs will further output “I don’t know!” to warn users not to trust it or verify it using other sources.

In “Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs“, presented at Findings of EMNLP 2023, we introduce ASPIRE — a novel framework meticulously designed to enhance the selective prediction capabilities of LLMs. ASPIRE fine-tunes LLMs on QA tasks via parameter-efficient fine-tuning, and trains them to evaluate whether their generated answers are correct. ASPIRE allows LLMs to output an answer along with a confidence score for that answer. Our experimental results demonstrate that ASPIRE significantly outperforms state-of-the-art selective prediction methods on a variety of QA datasets, such as the CoQA benchmark.

The mechanics of ASPIRE

Imagine teaching an LLM to not only answer questions but also evaluate those answers — akin to a student verifying their answers in the back of the textbook. That’s the essence of ASPIRE, which involves three stages: (1) task-specific tuning, (2) answer sampling, and (3) self-evaluation learning.

Task-specific tuning: ASPIRE performs task-specific tuning to train adaptable parameters (θp) while freezing the LLM. Given a training dataset for a generative task, it fine-tunes the pre-trained LLM to improve its prediction performance. Towards this end, parameter-efficient tuning techniques (e.g., soft prompt tuning and LoRA) might be employed to adapt the pre-trained LLM on the task, given their effectiveness in obtaining strong generalization with small amounts of target task data. Specifically, the LLM parameters (θ) are frozen and adaptable parameters (θp) are added for fine-tuning. Only θp are updated to minimize the standard LLM training loss (e.g., cross-entropy). Such fine-tuning can improve selective prediction performance because it not only improves the prediction accuracy, but also enhances the likelihood of correct output sequences.

Answer sampling: After task-specific tuning, ASPIRE uses the LLM with the learned θp to generate different answers for each training question and create a dataset for self-evaluation learning. We aim to generate output sequences that have a high likelihood. We use beam search as the decoding algorithm to generate high-likelihood output sequences and the Rouge-L metric to determine if the generated output sequence is correct.

Self-evaluation learning: After sampling high-likelihood outputs for each query, ASPIRE adds adaptable parameters (θs) and only fine-tunes θs for learning self-evaluation. Since the output sequence generation only depends on θ and θp, freezing θ and the learned θp can avoid changing the prediction behaviors of the LLM when learning self-evaluation. We optimize θs such that the adapted LLM can distinguish between correct and incorrect answers on their own.

The three stages of the ASPIRE framework.

In the proposed framework, θp and θs can be trained using any parameter-efficient tuning approach. In this work, we use soft prompt tuning, a simple yet effective mechanism for learning “soft prompts” to condition frozen language models to perform specific downstream tasks more effectively than traditional discrete text prompts. The driving force behind this approach lies in the recognition that if we can develop prompts that effectively stimulate self-evaluation, it should be possible to discover these prompts through soft prompt tuning in conjunction with targeted training objectives.

Implementation of the ASPIRE framework via soft prompt tuning. We first generate the answer to the question with the first soft prompt and then compute the learned self-evaluation score with the second soft prompt.

After training θp and θs, we obtain the prediction for the query via beam search decoding. We then define a selection score that combines the likelihood of the generated answer with the learned self-evaluation score (i.e., the likelihood of the prediction being correct for the query) to make selective predictions.

Results

To demonstrate ASPIRE’s efficacy, we evaluate it across three question-answering datasets — CoQA, TriviaQA, and SQuAD — using various open pre-trained transformer (OPT) models. By training θp with soft prompt tuning, we observed a substantial hike in the LLMs’ accuracy. For example, the OPT-2.7B model adapted with ASPIRE demonstrated improved performance over the larger, pre-trained OPT-30B model using the CoQA and SQuAD datasets. These results suggest that with suitable adaptations, smaller LLMs might have the capability to match or potentially surpass the accuracy of larger models in some scenarios.

When delving into the computation of selection scores with fixed model predictions, ASPIRE received a higher AUROC score (the probability that a randomly chosen correct output sequence has a higher selection score than a randomly chosen incorrect output sequence) than baseline methods across all datasets. For example, on the CoQA benchmark, ASPIRE improves the AUROC from 51.3% to 80.3% compared to the baselines.

An intriguing pattern emerged from the TriviaQA dataset evaluations. While the pre-trained OPT-30B model demonstrated higher baseline accuracy, its performance in selective prediction did not improve significantly when traditional self-evaluation methods — Self-eval and P(True) — were applied. In contrast, the smaller OPT-2.7B model, when enhanced with ASPIRE, outperformed in this aspect. This discrepancy underscores a vital insight: larger LLMs utilizing conventional self-evaluation techniques may not be as effective in selective prediction as smaller, ASPIRE-enhanced models.

Our experimental journey with ASPIRE underscores a pivotal shift in the landscape of LLMs: The capacity of a language model is not the be-all and end-all of its performance. Instead, the effectiveness of models can be drastically improved through strategic adaptations, allowing for more precise, confident predictions even in smaller models. As a result, ASPIRE stands as a testament to the potential of LLMs that can judiciously ascertain their own certainty and decisively outperform larger counterparts in selective prediction tasks.

Conclusion

In conclusion, ASPIRE is not just another framework; it’s a vision of a future where LLMs can be trusted partners in decision-making. By honing the selective prediction performance, we’re inching closer to realizing the full potential of AI in critical applications.

Our research has opened new doors, and we invite the community to build upon this foundation. We’re excited to see how ASPIRE will inspire the next generation of LLMs and beyond. To learn more about our findings, we encourage you to read our paper and join us in this thrilling journey towards creating a more reliable and self-aware AI.

Acknowledgments

We gratefully acknowledge the contributions of Sayna Ebrahimi, Sercan O Arik, Tomas Pfister, and Somesh Jha.

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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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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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