Summary of Aligning Llms Through Multi-perspective User Preference Ranking-based Feedback For Programming Question Answering, by Hongyu Yang et al.
Aligning LLMs through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering
by Hongyu Yang, Liyang He, Min Hou, Shuanghong Shen, Rui Li, Jiahui Hou, Jianhui Ma, Junda Zhao
First submitted to arxiv on: 27 May 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 A novel framework called Aligning LLMs through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering (ALMupQA) is proposed to generate user-focused responses for code community question answering (CCQA). This framework addresses challenges in fine-tuning Large Language Models (LLMs) for CCQA tasks, which involve multiple possible answers and varying user preferences. The approach starts with Multi-perspective Preference Ranking Alignment (MPRA), synthesizing varied user preferences based on answer characteristics from code communities. A Retrieval-augmented In-context Learning (RIL) module is also introduced to mitigate the problem of outdated answers by retrieving responses to similar questions from a question bank. The ALMupQA framework shows effectiveness in terms of accuracy and user preference, with nearly an 11% improvement in BLEU score compared to the base model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Code Community Question Answering (CCQA) is about helping people find answers to programming-related questions. This helps make software engineering and research more efficient. Large Language Models (LLMs) are great at answering questions, but they don’t always understand what people want. To solve this problem, researchers developed a new way to train LLMs called Aligning LLMs through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering (ALMupQA). This approach makes sure the answers match what people are looking for. |
Keywords
» Artificial intelligence » Alignment » Bleu » Fine tuning » Question answering
