Papers by Zidi Xiong
User-Assistant Bias in LLMs (2026.findings-acl)
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| Challenge: | Modern large language models are typically trained using structured role tags . asymmetries in training data associated with different role tags can potentially introduce inductive biases. |
| Approach: | They propose a task-agnostic benchmark to test user–assistant bias in large language models . they find human-preference alignment amplifies user bias, reasoning fine-tuning reduces it . |
| Outcome: | The proposed benchmark tests show that most instruction-tuned models exhibit strong user bias . human-preference alignment amplifies user bias, while reasoning fine-tuning reduces it. |
How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior (2026.acl-long)
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Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He, Zirui Liu, Jiliang Tang, Himabindu Lakkaraju, Zhen Xiang
| Challenge: | In practice, memory designs vary widely across agents due to their diverse objectives and functionalities. |
| Approach: | They conduct an empirical study on how memory management choices impact the LLM agents’ behavior, especially their long-term performance. |
| Outcome: | The proposed methods show that LLM agents display an experience-following property, which results in highly similar agent outputs. |
When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy (2025.findings-emnlp)
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| Challenge: | Recent Large Reasoning Models (LRMs) with thinking traces have shown strong performance on English reasoning tasks. |
| Approach: | They evaluate two leading LRMs with thinking traces on established benchmark XReasoning and propose directions for future research. |
| Outcome: | The proposed models often revert to English or produce fragmented reasoning in other languages, revealing a substantial gap in the capability of thinking in non-English languages. |