Summary of Recall, Retrieve and Reason: Towards Better In-context Relation Extraction, by Guozheng Li et al.
Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction
by Guozheng Li, Peng Wang, Wenjun Ke, Yikai Guo, Ke Ji, Ziyu Shang, Jiajun Liu, Zijie Xu
First submitted to arxiv on: 27 Apr 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 approach to relation extraction (RE) using large language models (LLMs). Although LLMs have shown impressive in-context learning abilities, they struggle with RE tasks. The authors identify two challenges: retrieving good demonstrations from training examples and enabling LLMs to exhibit strong ICL abilities in RE. To address these issues, the proposed framework, recall-retrieve-reason, synergizes LLMs with retrieval corpora to enable relevant retrieving and reliable in-context reasoning. The method distills consistently ontological knowledge from training datasets, generating relevant entity pairs grounded by retrieval corpora as valid queries. These entity pairs are then used to retrieve relevant training examples from the retrieval corpora as demonstrations for LLMs to conduct better ICL via instruction tuning. Experimental results demonstrate that this approach generates relevant and valid entity pairs, boosting ICL abilities of LLMs and achieving competitive or new state-of-the-art performance on sentence-level RE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using big language models to help computers understand relationships between things mentioned in texts. Right now, these models are not very good at this task compared to other methods that have been trained specifically for relation extraction. The problem is that it’s hard to find good examples of how the model should learn, and even when we do, the model doesn’t always use them correctly. To fix this, the authors propose a new way to train these models using other texts that contain relationships between things. This helps the model understand what makes one example more relevant than another, and it gets better at predicting relationships in text. |
Keywords
» Artificial intelligence » Boosting » Instruction tuning » Recall
