Summary of Llm Self-correction with Decrim: Decompose, Critique, and Refine For Enhanced Following Of Instructions with Multiple Constraints, by Thomas Palmeira Ferraz et al.
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints
by Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin, Haw-Shiuan Chang, Shereen Oraby, Sijia Liu, Vivek Subramanian, Tagyoung Chung, Mohit Bansal, Nanyun Peng
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel benchmark called RealInstruct is introduced to evaluate large language models’ (LLMs) ability to follow real-world multi-constrained instructions. This evaluation is based on queries from users asking AI assistants for assistance. The authors also explore model-based evaluation as a cost-effective alternative to human annotation. Despite the GPT-4 model’s high performance, it fails to meet at least one constraint in over 21% of cases, highlighting its limitations. To address this gap between open-source and proprietary models, the Decompose, Critique and Refine (DeCRIM) self-correction pipeline is proposed. DeCRIM enhances LLMs’ ability to follow constraints by decomposing the original instruction into a list of constraints and using a Critic model to decide when refinement is needed. The results show that DeCRIM improves Mistral’s performance on RealInstruct and IFEval even with weak feedback, and can outperform GPT-4 on both benchmarks with strong feedback. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RealInstruct is a new way to test how well AI language models follow instructions. These models often struggle when given multiple rules or constraints to follow. The current tests are based on fake data, but RealInstruct uses real questions people ask AI assistants. Even the best AI model, GPT-4, can’t always follow all the rules correctly (over 21% of the time). To help open-source models catch up, a new way to improve them is proposed. This method breaks down the instruction into smaller parts and asks another AI model to suggest when the language model needs to correct itself. The results show that this approach helps improve the performance of an open-source AI model called Mistral. |
Keywords
» Artificial intelligence » Gpt » Language model
