Papers by Tianxing Wu
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown remarkable performance on question-answering tasks due to their superior capabilities in natural language understanding and generation. |
| Approach: | They propose a structured taxonomy that categorizes the methodology of synthesizing LLMs and knowledge graphs for QA according to the categories of QA and the KG’s role when integrating with LLM. |
| Outcome: | The proposed taxonomy categorizes the methods according to the categories of QA and the KG’s role when integrating with LLMs. |
StratMem-Bench: Evaluating Strategic Memory Use in Virtual Character Conversation Beyond Factual Recall (2026.acl-long)
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| Challenge: | Current benchmarks for memory utilization ignore this nuance, treating memory as a static repository of facts rather than a dynamic resource to be strategically deployed in character-centric dialogues. |
| Approach: | They propose a benchmark to evaluate strategic memory use in character-centric dialogues . they use a dataset of 657 instances where virtual characters must navigate heterogeneous memory pools . |
| Outcome: | The proposed benchmarks show that all models perform well at distinguishing between required and irrelevant memories, but struggle once supportive memories are introduced into the decision process. |
CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction (2023.findings-acl)
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Xinnan Guo, Wentao Deng, Yongrui Chen, Yang Li, Mengdi Zhou, Guilin Qi, Tianxing Wu, Dong Yang, Liubin Wang, Yong Pan
| Challenge: | Existing methods to extract product features from unstructured text still suffer from problems . e-commerce platforms are focusing on multi-scale values, which can be confusing . |
| Approach: | They propose a pre-training technique to automatically obtain attribute value pairs from product descriptions to aid e-commerce. |
| Outcome: | The proposed method improves on the existing token-level masking strategy and achieves state-of-the-art on four benchmarks. |