Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions

Kernkonzepte

The author highlights the challenges faced by large language models in answering complex medical questions and emphasizes the importance of high-quality explanations in evaluating model performance.

Zusammenfassung

The content discusses the creation of two new datasets, JAMA Clinical Challenge and Medbullets, to evaluate large language models on challenging medical questions. The experiments show that these datasets are more difficult for models compared to previous benchmarks. The discrepancy between automatic and human evaluations of model-generated explanations underscores the need for improved evaluation metrics.

The study evaluates four LLMs on the datasets using various prompting strategies. Results indicate lower scores on the new tasks, suggesting a more realistic challenge for medical LLM research. The inclusion of high-quality expert-written explanations in the datasets aims to provide valuable insights beyond simple predictions.

Furthermore, the analysis reveals that in-context learning does not significantly enhance model adaptation to new tasks. Chain-of-Thought (CoT) prompting improves model reasoning but faces challenges with complex clinical cases. Evaluation metrics conflict in assessing model-generated explanations, highlighting the necessity for metrics aligned with human judgments.

Overall, the study introduces novel datasets for evaluating medical question answering with a focus on challenging clinical scenarios and emphasizes the importance of meaningful explanations in assessing model performance.

Statistiken

GPT-4 drops over 12% accuracy on Medbullets-4/5 compared to MedQA-4/5.
GPT-3.5, GPT-4, and PaLM 2 perform similarly on JAMA Clinical Challenge and Medbullets-4.
Human evaluation ranks GPT-4 as the best model for generating explanations.

Zitate

"The inconsistency between automatic and human evaluations of model-generated explanations highlights the necessity of developing evaluation metrics that can support future research on explainable medical QA."

Wichtige Erkenntnisse aus

Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions

by Hanjie Chen,... um arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18060.pdf

Tiefere Fragen

How do different prompting strategies impact large language models' performance in medical question answering?

Different prompting strategies have a significant impact on the performance of large language models (LLMs) in medical question answering. In the context of challenging medical questions, various promptings such as X→Y, X→RY, and XY∗→R were used to elicit responses from LLMs.

X→Y Prompting: This strategy involves asking the model to directly answer the question without any additional reasoning steps. It is a straightforward approach that tests the model's ability to provide accurate answers based on the input provided.

X→RY Prompting: With this strategy, LLMs engage in step-by-step reasoning before providing an answer. The CoT (Chain-of-Thought) method prompts models to walk through all choices in detail before making a prediction. This can help improve understanding and reasoning capabilities.

XY∗→R Prompting: Here, given the correct answer Y∗ along with other options, LLMs are asked to explain why a specific choice is correct while also addressing why other options are incorrect. This focuses on generating detailed explanations for each choice.
The impact of these prompting strategies varies across different datasets and models. For example:

In some cases, CoT prompting improved accuracy by guiding models through structured reasoning processes.
Few-shot learning using exemplars showed potential benefits for adapting quickly to new tasks.
Overall, choosing an appropriate prompting strategy is crucial for enhancing LLM performance in medical question answering tasks by guiding them towards more accurate predictions and explanations.

What are potential implications of discrepancies between automatic and human evaluations of model-generated explanations?

Discrepancies between automatic metrics and human evaluations of model-generated explanations in medical question answering have several important implications:

Evaluation Reliability: Automatic metrics like ROUGE-L or BERTScore may not always align with human judgments due to differences in how they assess explanation quality. This raises concerns about relying solely on automated evaluation methods without considering human feedback.

Model Interpretability: If automatic metrics favor certain models over others but do not reflect actual human preferences or understanding, it could lead to misinterpretations about which model performs best at generating high-quality explanations.

Bias Detection: Human evaluations can uncover biases or inaccuracies that automated metrics might overlook when assessing explanation quality generated by LLMs. Discrepancies highlight areas where models may need improvement regarding fairness or accuracy.

Research Direction: Addressing discrepancies can guide future research efforts towards developing better evaluation metrics that capture nuances missed by current automated methods while ensuring alignment with human judgment standards.

User Trust: Consistent evaluation results between humans and machines build user trust in AI systems' abilities to generate reliable explanations for complex tasks like clinical decision-making.

How can future research improve evaluation metrics to align more closely with human judgments in assessing explainable medical QA?

Future research aimed at improving evaluation metrics for explainable Medical Question Answering (QA) should focus on several key aspects:

Human-Centric Metrics Development: Develop new evaluation criteria specifically tailored towards capturing qualities valued by humans when assessing model-generated explanations such as coherence, relevance, completeness, clarity etc.

2 .Incorporating Subjectivity Analysis: Consider incorporating subjective assessments from domain experts or clinicians into metric design processes; this will help account for varying perspectives on what constitutes a good explanation within healthcare contexts
3 .Fine-grained Evaluation Criteria: Define fine-grained criteria that evaluate individual components of an explanation separately (e.g., correctness per option), allowing for nuanced assessment beyond overall quality scores
4 .Diverse Annotation Panels: Ensure diverse representation among annotators evaluating model outputs; including individuals from various backgrounds ensures comprehensive feedback reflecting real-world diversity
5 .**Iterative Metric Refinement Process: Implement iterative refinement cycles where newly proposed metric designs undergo validation against benchmark datasets followed by adjustments based on feedback received
6 .**Benchmark Dataset Expansion: Expand existing benchmark datasets with larger sample sizes covering diverse clinical scenarios; this will enable robust testing grounds for evaluating updated metric performances
By focusing on these areas during metric development processes researchers can create more effective tools aligned closely with desired outcomes facilitating enhanced assessment practices within Explainable Medical QA domains