Summary of Raw Text Is All You Need: Knowledge-intensive Multi-turn Instruction Tuning For Large Language Model, by Xia Hou et al.
Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model
by Xia Hou, Qifeng Li, Jian Yang, Tongliang Li, Linzheng Chai, Xianjie Wu, Hangyuan Ji, Zhoujun Li, Jixuan Nie, Jingbo Dun, Wenfeng Song
First submitted to arxiv on: 3 Jul 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 This paper presents a novel framework called R2S that utilizes CoD-Chain of Dialogue logic to generate knowledge-intensive multi-turn dialogues for instruction tuning. The approach integrates raw documents from open-source datasets and domain-specific web-crawled documents into the K-BENCH benchmark, covering areas like Wikipedia (English), Science (Chinese), and Artifacts (Chinese). By deciding the logic flow of the current dialogue and prompting large language models (LLMs) to produce key phrases for sourcing response content, R2S enables the creation of the G I NSTRUCT instruction dataset. This dataset is then used to fine-tune a model designed to transform raw documents into structured multi-turn dialogues, injecting comprehensive domain knowledge into the SFT model for enhanced instruction tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn how to have conversations with people. They want these conversations to be helpful and accurate. To make this happen, they’re creating new ways to teach computers about specific topics like science or art. The approach uses a special logic system called CoD-Chain to guide the computer’s responses. This makes it possible for computers to create complex dialogues that are similar to how humans talk. |
Keywords
» Artificial intelligence » Instruction tuning » Prompting
