Summary of Supportiveness-based Knowledge Rewriting For Retrieval-augmented Language Modeling, by Zile Qiao et al.
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling
by Zile Qiao, Wei Ye, Yong Jiang, Tong Mo, Pengjun Xie, Weiping Li, Fei Huang, Shikun Zhang
First submitted to arxiv on: 12 Jun 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 introduces Supportiveness-based Knowledge Rewriting (SKR), a robust and pluggable knowledge rewriter optimized for Language Model Generation (LLM). RALMs have shown potential in mitigating limitations of implicit knowledge in LLMs, but this can lead to unreliable or misleading knowledge retrieval. SKR addresses this by considering the perplexity impact of augmented knowledge on response text using the novel concept of “supportiveness”. A training data curation strategy is designed to filter out poor rewrites and improve data efficacy. The direct preference optimization (DPO) algorithm aligns generated rewrites to optimal supportiveness, guiding the model to summarize content that improves final responses. SKR demonstrates effectiveness and superiority across six popular knowledge-intensive tasks and four LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help language models learn more effectively. Language models are computers that can understand and generate human-like text, but they can have limitations when it comes to learning new information. This new method, called Supportiveness-based Knowledge Rewriting (SKR), helps fix these problems by making sure the information retrieved is helpful and accurate. It does this by considering how well the new information will help the language model make good responses. The method has been tested on many tasks and has shown that it can be more effective than other methods currently available. |
Keywords
» Artificial intelligence » Language model » Optimization » Perplexity
