Summary of Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives, by Runcong Zhao et al.
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives
by Runcong Zhao, Qinglin Zhu, Hainiu Xu, Jiazheng Li, Yuxiang Zhou, Yulan He, Lin Gui
First submitted to arxiv on: 16 Feb 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 addresses a critical gap in existing datasets for narrative understanding by introducing the Conan benchmark, designed specifically for extracting and analyzing intricate character relation graphs from detective narratives. The new dataset is crafted to reflect the complexity and uncertainty of relationships in real-life social scenarios, with hierarchical relationship categories and manual annotations capturing both public and secret relationships from various characters’ perspectives. Experiments with advanced Large Language Models (LLMs) like GPT-3.5, GPT-4, and Llama2 reveal their limitations in inferring complex relationships and handling longer narratives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a big difference by creating a special dataset called Conan that helps computers understand complicated relationships between characters in detective stories. Right now, most datasets are too simple and don’t reflect how people really interact with each other. Conan is different because it includes lots of information about both public and secret relationships between characters. Scientists tested powerful computer models on this new dataset and found out they’re not very good at understanding complicated relationships or long stories. |
Keywords
» Artificial intelligence » Gpt
