Summary of Meta In-context Learning Makes Large Language Models Better Zero and Few-shot Relation Extractors, by Guozheng Li et al.
Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors
by Guozheng Li, Peng Wang, Jiajun Liu, Yikai Guo, Ke Ji, Ziyu Shang, 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 new approach called Micre (Meta In-Context learning of LLMs for Relation Extraction) to improve large language models’ (LLMs’) zero and few-shot performance in relation extraction (RE). The authors introduce a meta-training framework that conditions an LLM on a diverse collection of RE datasets, enabling the model to learn new tasks in context without updating parameters or using task-specific templates. The framework is evaluated on various LLMs with different scales and 12 public RE datasets, demonstrating comparable or superior performance compared to baselines like supervised fine-tuning and typical in-context learning methods. The results show that Micre’s gains are particularly significant for larger model scales and highlight the importance of using a diverse set of meta-training datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Micre is a new way to help computers understand relationships between things, like people or places, in text. Currently, big language models struggle to learn these relationships without any training, but Micre can make them better. This happens by teaching the model to learn from many different texts about relationships and then using this knowledge to learn new texts. The results show that Micre makes the models more accurate, especially when they are very good at understanding language. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Supervised
