Papers by Yichen Yang
ThinkPersona: Thinking with Persona Graphs for Faithful Individualized Role-Playing (2026.acl-long)
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| Challenge: | Large Language Models are increasingly utilized as role-playing agents to simulate personas in interactive settings. |
| Approach: | They propose a role-playing agent trained to explicitly ground responses in individual identity. |
| Outcome: | The proposed agent can generate persona-consistent responses in long-context dialogues while maintaining general instruction-following capabilities. |
Understanding Secret Leakage Risks in Code LLMs: A Tokenization Perspective (2026.findings-acl)
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| Challenge: | Code Large Language Models (CLLMs) are reshaping how software is built, maintained, and evolved. |
| Approach: | They propose to use BPE tokenization to inadvertently leak code secrets . they propose to mitigate the gibberish bias by using a newer tokenizer . |
| Outcome: | The proposed model is based on a novel method that can be used to detect and mitigate gibberish bias in CLLMs. |
IEvoAgent: Evolving Conversational Agent based on User Implicit Feedback (2026.acl-long)
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| Challenge: | Existing approaches to optimize conversational agents often rely on explicit preference pairs and expert evaluations. |
| Approach: | They propose a conversational agent framework that leverages the structured dependency between agent responses and user reactions to extract implicit feedback. |
| Outcome: | The proposed framework improves on MT-Bench-101, WildBench, and FB-Bech, and shows that mining implicit feedback supports better multi-turn alignment under evolving user preferences. |
On Safety Risks in Experience-Driven Self-Evolving Agents (2026.findings-acl)
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Weixiang Zhao, Yichen Zhang, Yingshuo Wang, Yang Deng, Yanyan Zhao, Xuda Zhi, Yongbo Huang, Hao He, Wanxiang Che, Bing Qin, Ting Liu
| Challenge: | Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks. |
| Approach: | They investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments. |
| Outcome: | The findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation. |
Improving Pacing in Long-Form Story Planning (2023.findings-emnlp)
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| Challenge: | Existing systems for writing long-form stories suffer from unnatural pacing, whether glossing over important events or over-elaborating on insignificant details. |
| Approach: | They propose a system that uses a concreteness evaluator to judge which of two events is more concrete. |
| Outcome: | The proposed system improves pacing when automatically generating story outlines. |
Can Large Language Models Translate Unseen Languages in Underrepresented Scripts? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance in machine translation, but struggle with unseen low-resource languages. |
| Approach: | They propose a benchmark to evaluate translation for Mongolian and Yi using linguistic resources. |
| Outcome: | The proposed model can translate Mongolian (in traditional script) and Yi with the help of linguistic resources, but is limited in its ability to handle these languages effectively. |
Synonym-unaware Fast Adversarial Training against Textual Adversarial Attacks (2025.findings-naacl)
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| Challenge: | Existing adversarial defense methods rely on predetermined linguistic knowledge and assume that attackers’ synonym candidates are known, which is often unrealistic. |
| Approach: | They propose a Fast Adversarial Training method that leverages single-step perturbation generation and effective perturbation initialization to improve model robustness without requiring synonym awareness. |
| Outcome: | Experiments show that the proposed method outperforms existing models under character-level and word-level attacks while still maintaining the correct syntax. |
Robustness-Aware Word Embedding Improves Certified Robustness to Adversarial Word Substitutions (2023.findings-acl)
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| Challenge: | Embedding interval bound constraint is important for NLP models to be certified robust, but adversarial examples can be crafted by synonym substitutions. |
| Approach: | They propose a triplet loss to train robustness-aware word embeddings for better certified robustness. |
| Outcome: | The proposed method outperforms state-of-the-art certified defense baselines and generalizes well to unseen substitutions. |
FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language Model (2024.emnlp-industry)
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| Challenge: | Unlike other modalities, speech has unique temporal dependencies, making efficient inference methods unexplored. |
| Approach: | They propose a weighted token merging framework specifically designed for speech-related tasks to improve the trade-off between efficiency and performance. |
| Outcome: | The proposed method achieves state-of-the-art efficiency-performance trade-off on speech-related tasks. |
Token-level Proximal Policy Optimization for Query Generation (2025.emnlp-main)
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Yichen Ouyang, Lu Wang, Fangkai Yang, Pu Zhao, Chenghua Huang, Jianfeng Liu, Bochen Pang, Yaming Yang, Yuefeng Zhan, Hao Sun, Qingwei Lin, Saravan Rajmohan, Weiwei Deng, Dongmei Zhang, Feng Sun
| Challenge: | Large Language Models (LLMs) have improved search engines and recommendation systems through their text understanding capabilities. |
| Approach: | They propose a token-level proximal policy optimization approach to empower LLMs to perform better in query generation through fine-tuning. |
| Outcome: | The proposed approach outperforms existing LLMs on an open-source and industrial dataset. |
On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)
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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
| Challenge: | under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors. |
| Approach: | They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients . |
| Outcome: | The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets. |
Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement (2025.acl-long)
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| Challenge: | Recent research has shown that large language models (LLMs) can enhance translation quality through self-refinement. |
| Approach: | They propose to extend translation refinement from sentence-level to document-level by using document-to-document (Doc2Doc) translations. |
| Outcome: | The proposed method improves translation quality across ten translation tasks with LLaMA-3-8B-Instruct and Mistral-Nemo-Instru. |
Identifying and Mitigating Social Bias Knowledge in Language Models (2025.findings-naacl)
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| Challenge: | Existing methods for debiasing may generate incorrect or nonsensical predictions but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions. |
| Approach: | They propose a framework that identifies encoding locations of biases within language models and then applies the Fairness-Stamp (FAST) they also propose 'BiaScope' to evaluate the retention of commonsense knowledge and generalization across paraphrased social biase. |
| Outcome: | The proposed framework surpasses state-of-the-art baselines with superior debiasing performance while not compromising the overall model capability for knowledge retention and prediction. |
Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning (2026.acl-long)
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Junpeng Ding, Zichen Tang, Haihong E, Mengyuan Ji, Yang Liu, Haolin Tian, Haiyang Sun, Pengqi Sun, Yang Xu, Yichen Liu, Haocheng Gao, Zijie Xi, Ruomeng Jiang, Peizhi Zhao, Rongjin Li, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Jintong Chen, Siying Lin
| Challenge: | Xu and Peng, 2025) . . SPUR is a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images. |
| Approach: | They propose to use 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images to evaluate the visual perception of multimodal large language models (MLLMs) . they also propose to utilize cross-panel relation understanding to evaluate MLLM’s ability to decipher intricate cross-panel relations. |
| Outcome: | The proposed model is based on 4,264 question-answering pairs derived from 1,084 expert-curated images. |
3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding (2023.emnlp-main)
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| Challenge: | 3D visual grounding aims to localize the desired objects in a 3D point cloud by a free-form language description. |
| Approach: | They propose a relation-aware framework which captures relative spatial relationships between objects and enhances object attributes. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three benchmarks . it captures relative spatial relationships between objects and enhances object attributes . |