Papers by Chen-Yu Hsu
Magnet: Multi-turn Tool-use Data Synthesis and Distillation via Graph Translation (2025.acl-long)
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Fan Yin, Zifeng Wang, I-Hung Hsu, Jun Yan, Ke Jiang, Yanfei Chen, Jindong Gu, Long Le, Kai-Wei Chang, Chen-Yu Lee, Hamid Palangi, Tomas Pfister
| Challenge: | Large language models have been shown to be effective in multi-turn interactions . however, their performance may be limited in complex, multi-turned interactions involving users and multiple tools. |
| Approach: | They propose a framework for synthesizing high-quality training trajectories to enhance the function calling capability of large language model agents in multi-turn conversations with humans. |
| Outcome: | The proposed model outperforms the teacher model by 68.01 on BFCL-v3 and 73.30 on ToolQuery. |
SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback (2026.findings-eacl)
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Fangyuan Xu, Rujun Han, Yanfei Chen, Zifeng Wang, I-Hung Hsu, Jun Yan, Vishy Tirumalashetty, Eunsol Choi, Tomas Pfister, Chen-Yu Lee
| Challenge: | High-quality, complex question-answer pairs are pivotal for training and evaluating capable deep search agents. |
| Approach: | They propose a pipeline that generates high-quality, difficulty-controlled deep search question-answer pairs for a given corpus and a target difficulty level. |
| Outcome: | The proposed pipeline generates high-quality, difficulty-controlled deep search question-answer pairs for a given corpus and a target difficulty level. |
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents (2025.acl-long)
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Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han, Zifeng Wang, Long Le, Yiwen Song, Yanfei Chen, Hamid Palangi, George Lee, Anand Rajan Iyer, Tianlong Chen, Huan Liu, Chen-Yu Lee, Tomas Pfister
| Challenge: | Existing approaches to long-term dialogue memory management fail to capture the natural semantic structure of conversations, leading to fragmented and incomplete representations. |
| Approach: | They propose a mechanism that integrates forward- and backward-looking reflections into a personalized memory bank for effective future retrieval. |
| Outcome: | The proposed mechanism outperforms state-of-the-art benchmarks on a long-term dialogue memory model. |
CaLM: Contrasting Large and Small Language Models to Verify Grounded Generation (2024.findings-acl)
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| Challenge: | Existing methods to generate grounded responses are prone to errors due to the irrelevancy of input documents. |
| Approach: | They propose a framework that leverages the insight that a robust grounded response should be consistent with information derived solely from its cited sources. |
| Outcome: | Experiments on three open-domain question-answering datasets show that the proposed framework improves performance by 1.5% to 7% without any model fine-tuning. |
Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling (2024.findings-eacl)
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| Challenge: | Existing sparse retrieval methods often yield inferior performance in multilingual retrieval, requiring a large amount of paired data, which is costly. |
| Approach: | They propose an Unsupervised Multilingual dense Retriever trained without paired data which iteratively improves performance of multilingual retrievers. |
| Outcome: | The proposed framework outperforms supervised baselines on two benchmark datasets and shows that iterative training improves the performance. |