Papers by Hanwen Xu
Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs? (2024.naacl-long)
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| Challenge: | Existing large language models lack knowledge of nuanced, domain-specific details and are susceptible to hallucinations. |
| Approach: | They construct a benchmark that measures head, torso, and tail facts in terms of popularity. |
| Outcome: | The proposed model is based on 18K question-answer pairs regarding head, torso, and tail facts in terms of popularity. |
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks (2022.emnlp-main)
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| Challenge: | Existing meta-path generation methods cannot fully exploit rich textual information in HINs. |
| Approach: | They propose a text-infilling-based approach to generate meta-paths from textual information in HINs. |
| Outcome: | The proposed approach can classify edges in the zero-shot setting, where existing methods cannot generate meta-paths. |
TPS-Bench: Evaluating AI Agents’ Tool Planning & Scheduling Abilities in Compounding Tasks (2026.acl-long)
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| Challenge: | Large language model (LLM) agents have demonstrated strong problem-solving competence across domains like research and coding. |
| Approach: | They propose to use a tool repository to analyze the ability of large language model agents to solve complex problems. |
| Outcome: | The proposed model outperforms open-source and closed-source models in task completion rate and efficiency. |