TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation (2410.03608v1)

Abstract: Given the widespread adoption and usage of LLMs, it is crucial to have flexible and interpretable evaluations of their instruction-following ability. Preference judgments between model outputs have become the de facto evaluation standard, despite distilling complex, multi-faceted preferences into a single ranking. Furthermore, as human annotation is slow and costly, LLMs are increasingly used to make these judgments, at the expense of reliability and interpretability. In this work, we propose TICK (Targeted Instruct-evaluation with ChecKlists), a fully automated, interpretable evaluation protocol that structures evaluations with LLM-generated, instruction-specific checklists. We first show that, given an instruction, LLMs can reliably produce high-quality, tailored evaluation checklists that decompose the instruction into a series of YES/NO questions. Each question asks whether a candidate response meets a specific requirement of the instruction. We demonstrate that using TICK leads to a significant increase (46.4% $\to$ 52.2%) in the frequency of exact agreements between LLM judgements and human preferences, as compared to having an LLM directly score an output. We then show that STICK (Self-TICK) can be used to improve generation quality across multiple benchmarks via self-refinement and Best-of-N selection. STICK self-refinement on LiveBench reasoning tasks leads to an absolute gain of $+$7.8%, whilst Best-of-N selection with STICK attains $+$6.3% absolute improvement on the real-world instruction dataset, WildBench. In light of this, structured, multi-faceted self-improvement is shown to be a promising way to further advance LLM capabilities. Finally, by providing LLM-generated checklists to human evaluators tasked with directly scoring LLM responses to WildBench instructions, we notably increase inter-annotator agreement (0.194 $\to$ 0.256).

• The paper introduces TICK, an automated checklist protocol that boosts LLM evaluation alignment by 5.8%.
• The paper details STICK for self-improvement, achieving gains of 6.5% on InFoBench and 7.1% on WildBench.
• The paper demonstrates that checklist-guided Best-of-N selection improves human evaluation agreement, enhancing annotation reliability.

An Overview of "TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation"

The paper "TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation" investigates ways to enhance the evaluation and generation capabilities of LLMs. The authors introduce TICK (Targeted Instruct-evaluation with ChecKlists), an automatic and interpretable evaluation protocol that capitalizes on LLM-generated checklists tailored to specific instructions.

Main Contributions and Findings

1. Checklist Generation and Evaluation:
   • The authors propose a novel approach where LLMs generate checklists comprising YES/NO questions to assess specific facets of an instruction. This method breaks down complex instructions and provides a structured evaluation framework.
   • Through experimental validation on datasets such as Internal, InFoBench, and WildBench, it is demonstrated that TICK significantly improves agreement between LLM evaluations and human preferences, achieving a 5.8% increase in alignment.
2. Self-Improvement via STICK (Self-TICK):
   • STICK employs the generated checklists for in-context self-improvement, allowing LLMs to refine their responses iteratively.
   • Using this approach, Command-R+ demonstrated an absolute gain of 6.5% on InFoBench and 7.1% on WildBench. Such structured self-refinement shows marked improvements over traditional unstructured critique methods.
3. Best-of-N Selection:
   • TICK facilitates superior Best-of-N response selection by providing precise evaluation scores. This method outperforms traditional techniques like direct scoring and selection using a generic reward model.
4. Human Evaluation Assistance:
   • The paper also explores using LLM-generated checklists to support human evaluators, enhancing inter-annotator agreement from 0.194 to 0.256, demonstrating the practical benefits of structured evaluative aids in manual annotation processes.

Implications and Speculations

The research underlines the importance of structured, interpretable evaluation frameworks in AI systems, specifically in refining the outputs of LLMs. By breaking down the evaluation process into targeted, manageable components, TICK addresses concerns over the ambiguity and variability inherent in standard preference-based evaluations.

Practical Implications:

• This protocol is both cost-effective and scalable, making it feasible for rapid deployment across various domains where LLMs are employed.
• Enhanced alignment with human preferences has implications for deploying LLMs in sensitive applications such as conversational AI and automated content generation.

Theoretical Implications:

• It introduces a paradigm shift in evaluating LLMs, focusing on decomposing tasks into finer-grained evaluation metrics that can be automatically generated and processed.
• The findings pave the way for future work in developing more sophisticated and context-aware self-improvement mechanisms within LLM architectures.

Future Prospects:

• As LLMs evolve, the checklist generation and evaluation processes can harness emerging AI capabilities for more comprehensive multi-turn dialogue tasks and complex queries.
• Further research could explore dynamic checklist adaptation, where the checklist evolves with user inputs and AI learning, leading to even higher precision in evaluations.

In conclusion, the paper offers a significant contribution to the field of AI by enhancing the reliability and interpretability of LLM evaluations through structured, checklist-based methods. It sets a foundation for future advancements in AI model evaluation and self-improvement, promising developments in both practicality and theory.