Teaching Large Language Models to Use Criteria for Feedback Generation

Core Concepts

Teaching large language models to use criteria for feedback generation is essential for improving task performance and aligning with human values.

Abstract

The content discusses a framework that enables large language models (LLMs) to use comprehensive criteria for providing natural language feedback on task execution. By extracting criteria from guidelines and creating in-context demonstrations, the framework aims to improve the quality of generated feedback across various writing tasks. Humans follow criteria when executing tasks, which are used to assess task completion quality. Existing research often overlooks this aspect, leading to a proposal for a general framework that teaches LLMs to use criteria effectively. The study focuses on three real-world tasks: paper introduction writing, Python code writing, and Reddit post creation. Challenges in teaching LLMs include implicit criteria in guidelines and potential misapplication due to expertise requirements. The proposed model-in-the-loop approach extracts criteria and constructs demonstrations from guidelines to teach LLMs effectively. Evaluation metrics like validity, contextualization, constructiveness, and helpfulness are used to assess the impact of incorporating criteria and demonstrations. Experiment results show that adding criteria enhances the constructiveness of feedback texts but may reduce helpfulness in some cases. Providing both criteria and demonstrations yields mixed results compared to using only one approach. Overall, teaching LLMs to use criteria can lead to more insightful critiques and suggestions in generated feedback. The study also explores different teaching strategies with various LLMs across different writing tasks, highlighting the importance of incorporating comprehensive criteria for effective feedback generation.

Stats

We propose a new framework LLMCRIT for obtaining scalable oversight that takes advantage of criteria. Experiment results suggest that providing criteria allows the model to generate feedback that contains more critiques and suggestions. We release 83 criteria and 332 in-context demonstrations collected for three real-world writing tasks at https://github.com/yyy-Apple/LLMCrit.

Quotes

Key Insights Distilled From

LLMCRIT

by Weiz... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01069.pdf

Deeper Inquiries

How can teaching large language models to use comprehensive criteria enhance their performance beyond traditional methods?

Teaching large language models (LLMs) to use comprehensive criteria can significantly enhance their performance by providing a structured framework for evaluating tasks. By incorporating a wide range of criteria, LLMs can offer more detailed and nuanced feedback on task execution, leading to improved quality assessments. This approach allows LLMs to align with human expectations better and provide more valuable insights for iterative improvement. Additionally, using comprehensive criteria helps in scaling oversight and ensuring that the model's outputs are aligned with human values.

What counterarguments exist against the effectiveness of teaching LLMs using extensive criteria?

One counterargument against teaching LLMs using extensive criteria is the potential risk of overwhelming the model with too much information. Providing an excessive number of criteria could lead to confusion or inefficiency in generating feedback, as the model may struggle to prioritize relevant aspects or may become overly focused on minor details at the expense of overall task completion. Moreover, there might be challenges in interpreting complex or abstract criteria accurately, especially if they require domain-specific expertise that the model lacks.

How might teaching LLMs about using diverse sets of comprehensive criteria impact their ability to align with human values?

Teaching LLMs about using diverse sets of comprehensive criteria can significantly impact their ability to align with human values by promoting a more holistic understanding of task requirements and evaluation standards. By exposing models to a wide range of evaluative aspects through different sets of criteria, they gain a deeper insight into what constitutes high-quality task completion according to human standards. This exposure helps them generate feedback that reflects not only technical correctness but also considerations related to ethics, inclusivity, and other value-based principles important for human-centered decision-making.