Papers by Weiqi Wu
Towards Enhanced Immersion and Agency for LLM-based Interactive Drama (2025.acl-long)
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| Challenge: | Existing studies have focused on the role of immersion and agency in interactive drama. |
| Approach: | They propose a playwriting-guided generation method that helps LLMs craft dramatic stories with substantially improved structures and narrative quality. |
| Outcome: | The proposed method improves storytelling quality and immersion and agency, while allowing agents to refine their reactions to align with the player’s intentions. |
X-TURING: Towards an Enhanced and Efficient Turing Test for Long-Term Dialogue Agents (2025.acl-long)
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| Challenge: | Traditional Turing test limits each participant to one message at a time and requires constant human participation. |
| Approach: | They propose to enhance the original Turing test with a burst dialogue pattern, allowing more dynamic exchanges using consecutive messages. |
| Outcome: | The proposed test improves the original test with a burst dialogue pattern, allowing more dynamic exchanges using consecutive messages. |
COMBO: A Complete Benchmark for Open KG Canonicalization (2023.eacl-main)
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| Challenge: | Existing datasets for open KG canonicalization only provide gold entity-level canonization for noun phrases. |
| Approach: | They propose a complete benchmark for open KG canonicalization that provides gold ontology-level canonization for relation phrases and source sentences for extraction. |
| Outcome: | The proposed method improves relation canonicalization and ontology-level canonization of the noun phrase. |
From Role-Play to Drama-Interaction: An LLM Solution (2024.findings-acl)
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| Challenge: | aristotle defined drama as a form of storytelling that involves a predefined storyline, emotions and thoughts. |
| Approach: | They propose to use LLMs to create an immersive mode of storytelling . they propose to create a backbone drama LLM to drive the playing process . |
| Outcome: | The proposed model can be used to drive the playing process, the authors say . it can be compared with existing models and can be evaluated on multiple scenarios. |
ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents (2025.emnlp-main)
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| Challenge: | Existing benchmarks focus on image-based question answering (QA) but ignore the fundamental challenges of efficient retrieval, comprehension, and reasoning within dense visual documents. |
| Approach: | They propose a novel multi-agent RAG framework tailored for complex reasoning across visual documents that employs a Gaussian Mixture Model (GMM)-based hybrid strategy to handle multi-modal retrieval. |
| Outcome: | The proposed framework outperforms existing methods by over 10% on the competitive ViDoSeek benchmark. |
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters (2026.acl-industry)
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Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen, Feng Zhang
| Challenge: | Existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. |
| Approach: | They propose a conversation starter generation system that generates personalized starters to guide users into conversation without explicit user intent. |
| Outcome: | The proposed system improves user active days by +1.84 and click-through rate by +94.25 and has been deployed in production. |
Do PLMs Know and Understand Ontological Knowledge? (2023.acl-long)
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| Challenge: | Existing studies on pretrained language models focus mainly on factual knowledge, lacking a systematic probing of ontological knowledge. |
| Approach: | They investigate whether Pretrained Language Models store ontological knowledge and have a semantic un- derstanding of the knowledge rather than rote memorization of the surface form. |
| Outcome: | The proposed models can memorize certain ontological knowledge and perform logical reasoning with given knowledge according to ontological entailment rules. |
Open-Theatre: An Open-Source Toolkit for LLM-based Interactive Drama (2025.emnlp-demos)
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| Challenge: | Existing tools for creating, modifying, and experimenting with interactive dramas are limited. |
| Approach: | They propose an open-source toolkit for creating configurable LLM-based interactive drama. |
| Outcome: | The proposed toolkit enhances narrative coherence and realistic behavior in interactions with agents. |
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field (2022.emnlp-main)
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| Challenge: | Entity typing assigns semantic types to entities mentioned in text. |
| Approach: | They propose to use an undirected graphical model to formulate the UFET problem by combining unary potentials with a pairwise conditional random field model. |
| Outcome: | The proposed model outperforms the existing model with little cost and is thousands of times faster than the existing neural network module. |
Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization (2025.findings-naacl)
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| Challenge: | a new approach to timeline summarization is proposed for open-domain news content . large language models (LLMs) can be used to extract and organize news events from multiple documents . |
| Approach: | They propose a method to integrate Large Language Models into news timeline summarization by iterating on how events are linked and posing new questions. |
| Outcome: | The proposed system is able to generate and refresh chronological summaries based on documents retrieved in each round. |
Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset (2023.findings-emnlp)
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| Challenge: | Existing benchmarks or datasets require only a few steps of reasoning, making it difficult to analyse AI’s behaviour with reference to different problems within a specific topic in detail. |
| Approach: | They propose a conic10K math problem dataset that requires only a few steps of reasoning to be analysed. |
| Outcome: | The proposed dataset shows that existing language models exhibit weak performance on complex reasoning. |