Papers by Zhifeng Li
ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching (2026.findings-acl)
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Han Zhu, Wei Kang, Liyong Guo, Zengwei Yao, Fangjun Kuang, Weiji Zhuang, Zhaoqing Li, Zhifeng Han, Dong Zhang, Xin Zhang, Xingchen Song, Lingxuan Ye, Long Lin, Daniel Povey
| Challenge: | Existing autoregressive models for dialogue generation suffer from high latency and stability issues. |
| Approach: | They propose a non-autoregressive (NAR) zero-shot spoken dialogue generation model based on flow-matching. |
| Outcome: | The proposed model outperforms existing models in speech generation due to poor speech intelligibility and turn-taking precision. |
Rethinking Personality Assessment from Human-Agent Dialogues: Fewer Rounds May Be Better Than More (2025.findings-emnlp)
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| Challenge: | Existing personality assessment datasets based on natural language do not consider interactivity. |
| Approach: | They propose to use a Chinese dataset to study the effects of different interaction rounds and agent personalities on personality assessment. |
| Outcome: | The proposed dataset contains 1260 interaction rounds between humans and agents with different personalities. |
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)
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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke
| Challenge: | Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis. |
| Outcome: | SciAssess evaluates 11 LLMs on multiple tasks across scientific fields. |
Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment (2026.findings-acl)
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| Challenge: | Recent approaches to align LLMs with diverse human values are based on static linear scalarization or rigid gradient projection . however, these approaches often sacrifice potential global Pareto improvements to avoid transient local trade-offs. |
| Approach: | They propose a game-theoretic framework that reimagines alignment as a dynamic negotiation process. |
| Outcome: | The proposed framework breaks the deadlock between static linear scalarization and rigid gradient projection . it allows the model to escape local degradation and explore the distal Pareto-optimal frontier . |
Winnowing Knowledge for Multi-choice Question Answering (2021.findings-emnlp)
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| Challenge: | Existing reasoning models suffer from noises in retrieved knowledge . encoding methods that use commonsense knowledge are less effective . |
| Approach: | They propose a method which conducts interception and soft filtering to reduce noise . they use commonsense knowledge from Wikipedia and ConceptNet to encode questions and options . |
| Outcome: | The proposed method improves on commonsense question answering tasks compared to baselines . it is able to conduct interception and soft filtering to shield the encoder from noise . |
FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models (2025.emnlp-main)
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Hengxing Cai, Jinhan Dong, Jingjun Tan, Jingcheng Deng, Sihang Li, Zhifeng Gao, Haidong Wang, Zicheng Su, Agachai Sumalee, Renxin Zhong
| Challenge: | Existing methods for vision-and-language navigation struggle with insufficient multimodal fusion, weak generalization, and poor interpretability. |
| Approach: | They propose a framework for UAV vision-and-language navigation that integrates natural language instructions with visual observations to improve multimodal fusion and interpretability. |
| Outcome: | The proposed framework achieves state-of-the-art performance across all scenarios, with a 9.22% higher success rate than the strongest baseline in unseen environments. |
ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design (2026.findings-acl)
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Yutang Ge, Guojiang Zhao, Sihang Li, Zheng Cheng, Zifeng Zhao, Hanchen Xia, Guolin Ke, Linfeng Zhang, Zhifeng Gao, Yu Guang Wang
| Challenge: | Recent deep generative models have already shown encouraging * Equal contribution. |
| Approach: | They propose to use generic instruction-tuned LLMs as direct text-to-sequence generators to achieve this goal. |
| Outcome: | Recent studies show that reflection improves sequence quality and alignment while maintaining competitive foldability. |