Papers by Haofen Wang
ODDA: An OODA-Driven Diverse Data Augmentation Framework for Low-Resource Relation Extraction (2025.findings-acl)
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| Challenge: | Existing methods for low-resource relation extraction (LRE) lack diversity, leading to suboptimal performance. |
| Approach: | They propose to use large language models to augment relation extraction models by observing the RE model's behavior and replacing schema constraints with attribute constraints. |
| Outcome: | Experiments on three widely-used benchmarks show that the proposed method outperforms state-of-the-art methods while maintaining enhanced model stability. |
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. |
Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue (2024.findings-emnlp)
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| Challenge: | Existing RL methods focus on generation tasks while neglecting dialogue state tracking (DST) for understanding. |
| Approach: | They propose a method that integrates RL into both understanding and generation tasks by introducing step-by-step rewards throughout the token generation. |
| Outcome: | The proposed approach achieves state-of-the-art results on three widely used datasets. |
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. |