Summary of Enhancing Noise Robustness Of Retrieval-augmented Language Models with Adaptive Adversarial Training, by Feiteng Fang et al.
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training
by Feiteng Fang, Yuelin Bai, Shiwen Ni, Min Yang, Xiaojun Chen, Ruifeng Xu
First submitted to arxiv on: 31 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 addresses the limitations of Large Language Models (LLMs) in generating comprehensive and high-quality responses. LLMs, which have achieved significant capabilities, still struggle with issues like hallucination, outdated knowledge, and untraceable reasoning processes. The proposed solution, Retrieval-augmented generation (RAG), integrates external databases to mitigate these challenges. However, RAG can be hindered by retrieved passages that are inappropriate or noisy. To address this, the authors categorize retrieval noises into three distinct types, reflecting real-world environments, and analyze their impact on LLM robustness. The proposed RAAT approach combines adaptive adversarial training with multi-task learning to dynamically adjust the model’s training process in response to noise. Experimental results show that the LLaMA-2 7B model trained using RAAT achieves significant improvements in F1 and EM scores under diverse noise conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make language models better by using outside information. Language models are very good at understanding language, but they can sometimes make mistakes or give outdated answers. The authors propose a new way to use this outside information called Retrieval-augmented generation (RAG). RAG helps the model learn from external databases and avoid giving bad answers. However, the retrieved information can also be noisy or incorrect. To fix this, the authors identify three different types of noise that real-world models face and test how well their new approach works in these situations. The results show that their method, called RAAT, makes a big difference in improving the model’s performance. |
Keywords
» Artificial intelligence » Hallucination » Llama » Multi task » Rag » Retrieval augmented generation
