Summary of Linked: Eliciting, Filtering and Integrating Knowledge in Large Language Model For Commonsense Reasoning, by Jiachun Li et al.
LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning
by Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Jun Zhao
First submitted to arxiv on: 12 Oct 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 research paper proposes a novel method to improve large language models’ (LLMs) performance on knowledge-intensive tasks like commonsense reasoning. Current methods often rely on retrieving related knowledge or self-enhancement techniques, but they are limited by noisy knowledge and invalid reasoning issues. The proposed method, called LINKED, uses a reward model to filter out noise and a marginal consistent reasoning module to reduce invalid reasoning. Experimental results show significant improvements over state-of-the-art baselines (up to 9.0% accuracy). The paper also introduces a new metric for measuring the effectiveness of knowledge enhancement works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how to improve large language models’ performance on complex tasks like commonsense reasoning. Right now, these models don’t always get it right because they can pick up bad information or make mistakes. To fix this, the researchers created a new way to help the models learn from good information and avoid bad information. This method is called LINKED and it works by giving the model a reward when it uses good information and ignoring bad information. The results show that this method works really well, making the models 9% more accurate on average. |
