Summary of Aligning with Human Judgement: the Role Of Pairwise Preference in Large Language Model Evaluators, by Yinhong Liu et al.
Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators
by Yinhong Liu, Han Zhou, Zhijiang Guo, Ehsan Shareghi, Ivan Vulić, Anna Korhonen, Nigel Collier
First submitted to arxiv on: 25 Mar 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 The paper presents a novel approach to improving the evaluation capabilities of Large Language Models (LLMs) as automatic assessors of generated natural language. The authors first identify limitations in existing methods for mitigating biases in LLM evaluators, including misalignment with human assessments. To address this, they introduce Pairwise-preference Search (PAIRS), a rank aggregation method that employs LLMs to conduct pairwise comparisons and efficiently ranks candidate texts globally. PAIRS achieves state-of-the-art performance on representative evaluation tasks in long-form generations and demonstrates significant improvements over direct scoring. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve the accuracy of Large Language Models (LLMs) when evaluating generated natural language. Right now, these models can be biased and struggle to agree with human evaluations. The researchers looked at how well current methods work for fixing this bias and found that they’re not good enough. To solve this problem, they created a new method called Pairwise-preference Search (PAIRS). PAIRS helps LLMs compare texts in pairs and then rank them based on their preferences. This approach works really well and is better than the usual way of scoring text. |
