Papers by Yijiang Dong
CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding (2023.findings-acl)
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| Challenge: | Story generation and understanding has seen a surge in neurosymbolic work . symbolic methods are expensive and require a lot of time and expertise . |
| Approach: | They use Code-LLMs to bootstrap the use of symbolic methods for story understanding . they show that they can beat current LLM techniques on pre-existing stories with minimal hand engineering . |
| Outcome: | The proposed system beats state-of-the-art structured LLM techniques on pre-existing story understanding tasks with minimal hand engineering. |
Can LLM be a Personalized Judge? (2024.findings-emnlp)
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| Challenge: | a new study examines the reliability of large language models (LLMs) for personalization and role-playing evaluation without examining its validity. |
| Approach: | They investigate the reliability of LLM-as-a-Personalized-Judge for personalization . they find that personas provided to LLMs have limited predictive power . |
| Outcome: | The proposed model is less reliable than previously thought, the authors show . human annotation reveals that third-person crowd worker evaluations of personalized preferences are even worse than LLM predictions. |