Summary of Toward General Instruction-following Alignment For Retrieval-augmented Generation, by Guanting Dong et al.
Toward General Instruction-Following Alignment for Retrieval-Augmented Generation
by Guanting Dong, Xiaoshuai Song, Yutao Zhu, Runqi Qiao, Zhicheng Dou, Ji-Rong Wen
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 This research paper proposes a novel pipeline, VIF-RAG, to assess and improve instruction-following alignment in Retrieval-Augmented Generation (RAG) systems. Despite the advancements in Large Language Models (LLMs), this area remains under-explored. The proposed pipeline includes manual creation of atomic instructions, combination rules for synthesizing complex instructions, supervised models for instruction rewriting, and automated verification of instruction quality. The authors also introduce the FollowRAG Benchmark, which consists of approximately 3K test samples covering various categories of general instruction constraints and four knowledge-intensive QA datasets. This benchmark can seamlessly integrate with different RAG benchmarks. The results show that VIF-RAG enhances LLM performance across a range of general instruction constraints while leveraging its capabilities in RAG scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG systems need to follow instructions well, but there’s limited research on how to make them better at this. This paper presents a new way to do this called VIF-RAG. It involves creating simple instructions and combining them to make more complex ones. The authors also created a benchmark test to see if the system can follow different types of instructions correctly. They found that using VIF-RAG makes language models work better at following instructions, especially in situations where they need to generate text based on what someone else has said. |
Keywords
» Artificial intelligence » Alignment » Rag » Retrieval augmented generation » Supervised
