Summary of Ahp-powered Llm Reasoning For Multi-criteria Evaluation Of Open-ended Responses, by Xiaotian Lu et al.
AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses
by Xiaotian Lu, Jiyi Li, Koh Takeuchi, Hisashi Kashima
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 method leverages large language models (LLMs) and the analytic hierarchy process (AHP) to assess answers to open-ended questions, showcasing improved performance compared to four baselines. The approach generates multiple evaluation criteria for a question using LLMs, which are then used for pairwise comparisons under each criterion with LLMs. This yields scores for each answer in AHP. Experiments were conducted on four datasets using ChatGPT-3.5-turbo and GPT-4, demonstrating that the proposed method more closely aligns with human judgment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve our ability to evaluate answers to open-ended questions. Currently, large language models (LLMs) are great at answering many types of questions, but they struggle when it comes to open-ended questions that require creative and unique responses. The proposed method uses LLMs to help decide what makes a good answer to an open-ended question. This is done by generating criteria for evaluating answers and then using those criteria to compare different answers. |
Keywords
» Artificial intelligence » Gpt
