Summary of Ticking All the Boxes: Generated Checklists Improve Llm Evaluation and Generation, by Jonathan Cook et al.
TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation
by Jonathan Cook, Tim Rocktäschel, Jakob Foerster, Dennis Aumiller, Alex Wang
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TICK (Targeted Instruct-evaluation with ChecKlists) protocol is a fully automated and interpretable evaluation method for Large Language Models’ instruction-following abilities. The approach structures evaluations with LLM-generated, instruction-specific checklists that decompose the instruction into YES/NO questions. This leads to a significant increase in exact agreements between LLM judgments and human preferences (46.4% → 52.2%). Additionally, STICK (Self-TICK) is used for self-refinement and Best-of-N selection, achieving absolute gains of +7.8% and +6.3%, respectively. The protocol shows promise for advancing LLM capabilities and increasing inter-annotator agreement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to test how well large language models follow instructions. Normally, we rely on humans to make these judgments, but that’s slow and expensive. Instead, the authors suggest using the model itself to create a checklist of questions that evaluate whether its own answers meet certain requirements. This approach leads to better agreement between the model’s judgment and human preferences. The researchers also show that this method can help improve the quality of the model’s generated text. |
