Summary of Bridging Context Gaps: Leveraging Coreference Resolution For Long Contextual Understanding, by Yanming Liu et al.
Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding
by Yanming Liu, Xinyue Peng, Jiannan Cao, Shi Bo, Yanxin Shen, Tianyu Du, Sheng Cheng, Xun Wang, Jianwei Yin, Xuhong Zhang
First submitted to arxiv on: 2 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 The paper proposes Long Question Coreference Adaptation (LQCA), a novel framework to improve the performance of large language models (LLMs) in question answering tasks involving lengthy contexts. The LQCA method tackles challenges arising from complexity and ambiguity by enhancing coreference resolution, allowing the model to effectively identify and manage references. This is achieved through four key steps: resolving sub-document coreferences, computing mention distances, defining a representative mention for coreference, and answering questions via mention replacement. Experimental results demonstrate positive outcomes on various LLMs and datasets, including notable improvements on OpenAI-o1-mini and GPT-4o models. The framework’s effectiveness lies in its ability to provide easier-to-handle partitions for LLMs, promoting better understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models understand longer texts and answer questions more accurately. The problem is that these models struggle when dealing with complex and ambiguous texts. To solve this issue, the authors created a new method called Long Question Coreference Adaptation (LQCA). It’s like a special tool that helps the model identify important words and phrases in the text, making it easier to understand. They tested this method on different language models and datasets, and the results show that it works really well. This is exciting because it could help us use these language models for even more tasks, like summarizing long documents or answering complex questions. |
Keywords
» Artificial intelligence » Coreference » Gpt » Question answering
