DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents (2303.17071v1)

Abstract: LLMs have emerged as valuable tools for many natural language understanding tasks. In safety-critical applications such as healthcare, the utility of these models is governed by their ability to generate outputs that are factually accurate and complete. In this work, we present dialog-enabled resolving agents (DERA). DERA is a paradigm made possible by the increased conversational abilities of LLMs, namely GPT-4. It provides a simple, interpretable forum for models to communicate feedback and iteratively improve output. We frame our dialog as a discussion between two agent types - a Researcher, who processes information and identifies crucial problem components, and a Decider, who has the autonomy to integrate the Researcher's information and makes judgments on the final output. We test DERA against three clinically-focused tasks. For medical conversation summarization and care plan generation, DERA shows significant improvement over the base GPT-4 performance in both human expert preference evaluations and quantitative metrics. In a new finding, we also show that GPT-4's performance (70%) on an open-ended version of the MedQA question-answering (QA) dataset (Jin et al. 2021, USMLE) is well above the passing level (60%), with DERA showing similar performance. We release the open-ended MEDQA dataset at https://github.com/curai/curai-research/tree/main/DERA.

• The paper demonstrates that a novel dialog-based framework using dedicated agents significantly improves factual accuracy and completeness in LLM outputs.
• It details how a Researcher extracts key data and a Decider refines responses, reducing hallucinations and omissions in clinical applications.
• Empirical evaluations show enhanced performance in medical summarization and care plan generation compared to standalone GPT-4, indicating promising paths for future research.

An Evaluation of Dialog-Enabled Resolving Agents (DERA) in Enhancing LLM Completions in Clinical Settings

The paper "DERA: Enhancing LLM Completions with Dialog-Enabled Resolving Agents" proposes a novel framework, DERA, designed to address the limitations of LLMs such as GPT-4, primarily in safety-critical domains like healthcare. The authors introduce a dialog-based approach, leveraging two types of agents—Researcher and Decider—to improve factual accuracy and completeness of LLM outputs through iterative feedback and resolution mechanisms.

Framework Overview

DERA is conceptualized around two specialized agents:

1. Researcher: This agent processes input data, extracting and examining vital components of the given problem.
2. Decider: This agent uses insights from the Researcher to formulate and refine the output, holding the responsibility for the final answer synthesis.

The DERA framework utilizes conversational capabilities of LLMs to support iterative discussions between the agents, aiming to refine outputs through deeper, role-centric analysis and decision-making.

Empirical Evaluation

The efficacy of DERA was assessed across three clinically-focused tasks: medical conversation summarization, care plan generation, and MedQA dataset performance. Key findings include:

• Summarization and Care Plan: DERA showed measurable improvements over standalone GPT-4 performance in both human expert evaluations and quantitative metrics.
• MedQA Performance: It is noteworthy that GPT-4 achieved 70% accuracy on an open-ended MedQA task, surpassing the 60% passing threshold for the USMLE. DERA's performance was similar in this setting.

Discussion

The implementation of DERA provided several insights into the role of structured dialog in enhancing LLM utility.

• Reduced Hallucination and Omission: The dialog-driven interaction enabled by DERA effectively mitigated common issues associated with LLMs, such as hallucinations and essential data omissions, by fostering rigorous vetting and synthesis of information.
• Adaptability to Long-form Text Generation: DERA was particularly effective in tasks requiring detailed responses, aligning with its design philosophy to leverage specialized dialog to accommodate complex generative requirements.
• Challenges in QA Contexts: While DERA did not significantly enhance performance on question-answering tasks relative to existing GPT-4 capabilities, it indicates that such dialog-based techniques may not be universally applicable across all types of tasks, especially when short, definitive text is required.

Implications and Future Research

The introduction of DERA provides avenues for enhanced interpretability and auditability of LLM outputs, crucial for domains where precision and accountability are paramount. The authors suggest potential expansions of this framework through human integration and tailoring the agent roles to align with varying problem spaces.

Moreover, the paper underscores the necessity for improved automated metric systems to objectively evaluate LLM-generated content. The current reliance on human qualitative feedback is beneficial yet insufficient for comprehensive evaluations, thus advocating for innovative approaches in benchmarking and validation processes.

In conclusion, DERA signifies an important development in leveraging LLMs, showcasing promising improvements in specific domains through an agent-based approach, while also recognizing limitations and areas ripe for future exploration. The careful consideration of task-specific agent dialogues highlights a nuanced approach to harnessing LLM capabilities, with broader implications for ensuring safety and efficacy in real-world applications.