Summary of Improving Retrieval Augmented Language Model with Self-reasoning, by Yuan Xia et al.
Improving Retrieval Augmented Language Model with Self-Reasoning
by Yuan Xia, Jingbo Zhou, Zhenhui Shi, Jun Chen, Haifeng Huang
First submitted to arxiv on: 29 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 The Retrieval-Augmented Language Model (RALM) has made significant progress in knowledge-intensive tasks by incorporating external knowledge during inference. However, challenges remain in implementing RALMs reliably and tracing their processes. This paper proposes a self-reasoning framework to improve the reliability and traceability of RALMs. The framework uses three processes: relevance-aware, evidence-aware selective, and trajectory analysis. Evaluations across four public datasets demonstrate the superiority of this method, outperforming existing state-of-the-art models and achieving comparable performance with GPT-4 using only 2,000 training samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a big discovery about language models! They found a way to make them more reliable and trustworthy by giving them a special task. The model uses three steps to figure out what it’s talking about: relevance, evidence, and analysis. This helps the model avoid making mistakes and gives us better answers. The scientists tested this new method on four different sets of questions and showed that it works really well! |
Keywords
» Artificial intelligence » Gpt » Inference » Language model
