Papers by Tingxu Han
Token-Budget-Aware LLM Reasoning (2025.findings-acl)
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| Challenge: | Existing methods to enhance reasoning capabilities of large language models incur significant overhead in token usage, leading to increased costs. |
| Approach: | They propose a token-budget-aware LLM reasoning framework that adjusts the number of reasoning tokens based on the reasoning complexity of each problem. |
| Outcome: | The proposed method reduces token costs in CoT reasoning with only a slight performance reduction. |
Debiasing LLMs by Masking Unfairness-Driving Attention Heads (2026.findings-acl)
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Tingxu Han, Wei Song, Ziqi Ding, Ziming Li, Chunrong Fang, Yuekang Li, Dongfang Liu, Zhenyu Chen, Zhenting Wang
| Challenge: | Existing work probes when biased outputs appear, but gives little insight into the mechanisms that generate them, leaving existing mitigations largely fragile. |
| Approach: | They propose a lightweight debiasing framework that detects bias heads and selectively masks only those heads that activate under DA and CoT. |
| Outcome: | The proposed framework reduces unfairness by 391.9%- 534.5% in both one- and two-turn dialogues. |