Papers by Chuchu Fan
Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) struggle to generate correct plans for multi-constraint planning problems . a recent study showed that large language models have significant potential in solving planning problems. |
| Approach: | They propose an LLM-based planning framework that formalizes and solves multi-constraint planning problems as constrained satisfiability problems. |
| Outcome: | The proposed framework achieves a success rate of 93.9% and is effective with diverse paraphrased prompts. |
Partial Or Complete, That’s The Question (N19-1)
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| Challenge: | Existing annotation schemes aim at acquiring completely annotated structures, but partial annotations can be costly and hinder learning. |
| Approach: | They propose a method to find out that learning from partial structures can sometimes outperform learning from complete ones. |
| Outcome: | The proposed method outperforms existing methods in three different structured learning tasks. |
PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling (2024.emnlp-main)
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| Challenge: | Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task. |
| Approach: | They propose a method to optimize prompts for LLM-driven multi-step tasks using a human-designed feedback rule. |
| Outcome: | The proposed method outperforms human-engineered prompts and several other prompt optimization methods on 11 representative multi-step tasks. |
A Meta-framework for Spatiotemporal Quantity Extraction from Text (2022.acl-long)
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| Challenge: | a meta-framework for news events that extracts quantities from text is proposed . a previous work on news events focused on extracting event mentions, attributes, and relationships . |
| Approach: | They propose a meta-framework for solving the NLP problem of spatiotemporal quantity extraction . they demonstrate the framework is general and extensible, and shareable crowdsourcing pipeline and baseline models are used . |
| Outcome: | The proposed framework is general and extensible, the authors say . it can extract quantity from news streams, quickly respond to emergencies, investigate incidents . |
NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models (2023.emnlp-main)
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| Challenge: | Temporal Logic (TL) can be used to specify complex high-level specifications for systems in many engineering domains. |
| Approach: | They propose a framework for translation between NL and TL using Large Language Models . they use a dataset to create a model with 23K NL-TL pairs and human annotation . |
| Outcome: | The proposed framework achieves higher accuracy (> 95%) using only 10% training data compared with baseline model. |