Papers by Wenshan Wu
Learning to Plan by Updating Natural Language (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance in basic natural language tasks. |
| Approach: | They propose a method that iteratively updates the task plan with new steps and behavioral instructions to guide LLMs to generate the correct solutions step by step. |
| Outcome: | The proposed method improves performance on five reasoning type tasks and can be directly applied to other LLMs. |
Low-code LLM: Graphical User Interface over Large Language Models (2024.naacl-demo)
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Yuzhe Cai, Shaoguang Mao, Wenshan Wu, Zehua Wang, Yaobo Liang, Tao Ge, Chenfei Wu, WangYou WangYou, Ting Song, Yan Xia, Nan Duan, Furu Wei
| Challenge: | Low-code LLM is a visual programming interface that allows users to incorporate their ideas into the process without writing trivial prompts. |
| Approach: | They propose a human-LLM interaction framework that incorporates low-code visual programming interactions to achieve more controllable and stable responses. |
| Outcome: | The proposed framework enables users to incorporate ideas into the process without writing trivial prompts. |
Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration (2024.naacl-long)
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| Challenge: | Existing work on LLMs that only enhance reasoning abilities, but which lack factual hallucination and slow-thinking capabilities, argues that SPP is a cognitive synergist. |
| Approach: | They propose a Solo Performance Prompting (SPP) that transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas. |
| Outcome: | The proposed model reduces factual hallucination and maintains strong reasoning abilities on three challenging tasks . |
Demystifying Data Organization for Enhanced LLM Training (2026.acl-long)
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Yalun Dai, Yangyu Huang, Tongshen Yang, Yonghan Wang, Xin Zhang, Wenshan Wu, Qihao Zhao, Hao Li, Yuanyuan Gao, Kim-Hui Yap, Scarlett Li
| Challenge: | Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation. |
| Approach: | They propose to reuse pre-computed sample-level scores originally generated for data efficiency and introduce two new data ordering methods to improve LLM training. |
| Outcome: | The proposed methods improve the stability and performance of LLM training. |
Smart Word Suggestions for Writing Assistance (2023.findings-acl)
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| Challenge: | Using word suggestions, writing assistance is a widely used application of natural language processing (NLP) . a task is performed to identify words or phrases that require improvement and provide substitution suggestions for each improvable target. |
| Approach: | They propose a task and benchmark to help writers improve word usage . they use human-labeled data and a distantly supervised dataset for testing . |
| Outcome: | The proposed task and benchmark aims to improve word usage in writing aids. |
ALYMPICS: LLM Agents Meet Game Theory (2025.coling-main)
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| Challenge: | Alympics provides a framework for simulating human-like strategic interactions with Large Language Model (LLM) agents. |
| Approach: | They propose a framework utilizing Large Language Models (LLM) agents for empirical game theory research. |
| Outcome: | The proposed framework can be used to study human-like strategic interactions with large language model (LLM) agents in a game on the multi-round auction of scarce survival resources. |