Papers by Yifan Zhou
Reflection on Knowledge Graph for Large Language Models Reasoning (2025.findings-acl)
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Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering. |
| Approach: | They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. |
| Outcome: | The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions. |
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)
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| Challenge: | Existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. |
| Approach: | They propose to deal with present and absent keyphrases separately with different mechanisms by using a hierarchical neural network with a pointing-based selector and a selection-guided generator. |
| Outcome: | The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks. |
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation (2024.eacl-long)
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| Challenge: | Existing non-autoregressive (NAR) text-to-text generation methods are unable to generate coherent and fluent texts due to discrete nature of text. |
| Approach: | They propose to integrate discrete diffusion models (DDM) into NAR text-to-text generation and integrate BART to improve the performance. |
| Outcome: | The proposed method outperforms competing methods and surpasses autoregressive methods on 7 datasets. |
ProQA: Structural Prompt-based Pre-training for Unified Question Answering (2022.naacl-main)
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Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan
| Challenge: | Existing QA research on question answering is focused on specific question types, knowledge domains, or reasoning skills. |
| Approach: | They propose a unified QA paradigm that solves various tasks through a single model. |
| Outcome: | The proposed model improves QA-centric ability on 11 QA benchmarks. |
Evaluating Object Hallucination in Large Vision-Language Models (2023.emnlp-main)
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| Challenge: | Large vision-language models (LVLMs) suffer from object hallucinations, i.e., they tend to generate objects inconsistent with the target images in the descriptions. |
| Approach: | They propose to integrate powerful large vision-language models (LVLMs) they propose a polling-based query method to evaluate object hallucination . |
| Outcome: | The proposed model can evaluate object hallucination in a more stable and flexible way. |
Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training (2026.acl-long)
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Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan, Xie Chen
| Challenge: | Existing models for fine-grained speaking styles are limited in terms of accuracy, coverage, and naturalness. |
| Approach: | They propose a model that pre-trains with coarse captions and annotates with a pipeline that grounds captions in audio. |
| Outcome: | The proposed model outperforms existing models with fine-grained style annotations . it integrates global and fine-granular supervision, enabling unified representations based on the proposed model . |
QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models (2025.emnlp-main)
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Jiajun Zhou, Yifan Yang, Kai Zhen, Ziyue Liu, Yequan Zhao, Ershad Banijamali, Athanasios Mouchtaris, Ngai Wong, Zheng Zhang
| Challenge: | Large Language Models (LLMs) are quantized to lower precision to reduce memory cost and latency in inference. |
| Approach: | They propose a quantized zeroth-order framework for fine-tuning Large Language Models (LLMs) using low-precision forward passes. |
| Outcome: | The proposed method achieves comparable results to first-order methods in FP8 and superior accuracy in INT8 and INT4 training. |
LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition (2025.emnlp-main)
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| Challenge: | Existing methods for understanding intents from multimodal signals exhibit limitations in their modality-level reliance, constraining relational reasoning over fine-grained semantics for complex intent understanding. |
| Approach: | They propose a method that harnesses the expansive knowledge of large language models to establish semantic foundations that boost smaller models’ relational reasoning performance. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multimodal intent and dialogue act recognition tasks and shows consistent performance gains across diverse semantic understanding scenarios. |
Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning (2026.acl-long)
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| Challenge: | Existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking. |
| Approach: | They propose a process-aware evaluation paradigm that uses a hierarchical rubric to evaluate the validity of the intermediate steps and the final result. |
| Outcome: | The proposed model achieves POC@1.0 only about 20% and exhibits significant outcome-hacking. |
PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)
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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang
| Challenge: | Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed . |
| Approach: | They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision. |
| Outcome: | The proposed framework reduces token usage while improving accuracy on math benchmarks. |
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability. |
| Approach: | They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality. |
| Outcome: | The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks. |
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing (2025.findings-acl)
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Yifan Lu, Jing Li, Yigeng Zhou, Yihui Zhang, Wenya Wang, Xiucheng Li, Meishan Zhang, Fangming Liu, Jun Yu, Min Zhang
| Challenge: | Existing knowledge editing methods for large language models (LLMs) suffer from over-editing, where detoxified models reject legitimate queries, compromising overall performance. |
| Approach: | They propose a toxicity-aware knowledge editing approach that dynamically detects toxic activation patterns during forward propagation and then routes computations through adaptive inter-layer pathways to mitigate toxicity effectively. |
| Outcome: | The proposed method outperforms existing methods on large language models and enhances the SafeEdit benchmark. |
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)
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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam
| Challenge: | Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others. |
| Approach: | They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM. |
| Outcome: | The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants. |
TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training (2026.acl-long)
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Yifan Wang, null Binbinliu, Fengze Liu, Yuanfan Guo, Jiyao Deng, Xuecheng Wu, Weidong Zhou, Xiaohuan Zhou, Taifeng Wang
| Challenge: | Static data mixing strategies in large language models are often suboptimal as they fail to adapt to the model’s evolving learning states. |
| Approach: | They propose a semi-dynamic data mixing framework that uses a key observation of influence ranking invariance to reduce computational overhead by 80% . |
| Outcome: | The proposed method reduces computational overhead by 80% and achieves an average performance gain of 2% across nine downstream benchmarks, effectively mitigating data under-digestion. |
ConFiguRe: Exploring Discourse-level Chinese Figures of Speech (2022.coling-1)
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| Challenge: | Figures of speech often deviate from their literal meanings to express deeper semantic implications. |
| Approach: | They propose a concept of figurative unit, which is the carrier of a figure, and build a Chinese corpus for Contextualized Figure Recognition. |
| Outcome: | The proposed model is based on 12 types of figures commonly used in Chinese . it shows that the proposed tasks are challenging for existing models . |
Large Language Models Are Poor Clinical Decision-Makers: A Comprehensive Benchmark (2024.emnlp-main)
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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David Clifton
| Challenge: | Existing studies focus on evaluating large language models in close-ended QA tasks, but many clinical decisions involve answering open-ended questions without pre-set options. |
| Approach: | They construct a benchmark to better understand large language models in the clinic . they use existing datasets to evaluate LLMs in clinical situations . |
| Outcome: | The proposed model outperforms human experts in multiple medical tasks. |
LLM Inductive Reasoning Through Multi-Agent Enhanced Monte Carlo Tree Search (2026.findings-acl)
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| Challenge: | Existing methods for enhancing inductive reasoning of large language models often lack explicit optimization guidance and effective error correction. |
| Approach: | They propose a plug-and-play test-time framework that integrates multi-agent coordination with Monte Carlo Tree Search to improve inductive reasoning. |
| Outcome: | The proposed framework outperforms existing methods on four benchmarks and shows consistent improvements on QWQ-32B and Deepseek-V3 . |
DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models (2024.emnlp-main)
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Ranchi Zhao, Zhen Thai, Yifan Zhang, Shengding Hu, Jie Zhou, Yunqi Ba, Jie Cai, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) are pre-trained on vast datasets composed of billions of tokens harvested from diverse text sources. |
| Approach: | They propose a data engineering method to refine the pretraining corpus through data rating, tagging and editing. |
| Outcome: | The proposed method improves the quality of the pretraining corpus by enhancing 100 billion tokens of the training corpus. |
RoR: Read-over-Read for Long Document Machine Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing models for machine reading comprehension are limited to individual chunks due to encoding length constraint. |
| Approach: | They propose a read-over-read method that expands the reading field from chunk to document by predicting regional answers for each chunk. |
| Outcome: | Extensive experiments on QuAC and TriviaQA show that the proposed model performs well for long document reading. |
OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)
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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
| Challenge: | Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability. |
| Approach: | They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability. |
| Outcome: | Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA. |
What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning (2025.coling-main)
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Yifan Du, Hangyu Guo, Kun Zhou, Wayne Xin Zhao, Jinpeng Wang, Chuyuan Wang, Mingchen Cai, Ruihua Song, Ji-Rong Wen
| Challenge: | Experimental results show that visual instruction tuning improves performance of Multi-modal Large Language Models (MLLMs) to extend the application scope of Large Language Modells, a surge of work augments LLMs with vision encoders to endow the ability of multi-modal cognition and reasoning. |
| Approach: | They propose a systematic approach to create high-quality visual reasoning instructions using a synthesize-complicate-reformulate paradigm. |
| Outcome: | The proposed method improves performance of MLLMs by 27.86% and 27.60% on MME-Perception and MME Cognition. |
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models (2024.naacl-long)
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| Challenge: | Existing methods for parameter-efficient fine-tuning are limited by the growing number of trainable parameters with the rapid deployment of Large Language Models (LLMs). |
| Approach: | They propose a parameter-efficient framework that reduces trainable parameters through tensor-train decomposition. |
| Outcome: | The proposed methods achieve comparable or better performance than most widely used methods with up to 100 fewer parameters on the LLaMA-2-7B models. |
Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning (2026.acl-long)
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Zelin Tan, Hejia Geng, Xiaohang Yu, Mulei Zhang, Guancheng Wan, Yifan Zhou, Qiang He, Xiangyuan Xue, Heng Zhou, Yutao Fan, Zhong-Zhi Li, Zaibin Zhang, Guibin Zhang, Chen Zhang, Zhenfei Yin, Philip Torr, Lei Bai
| Challenge: | elucidating scaling laws for large language models (LLMs) during pre-training remains unexplored. |
| Approach: | They characterize how model scale, data, and compute interact during pre-training . they find that large models consistently demonstrate superior compute and data efficiency . |
| Outcome: | The proposed scaling laws offer practical guidance for scaling reasoning capabilities through reinforcement learning post-training. |
Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models (2023.emnlp-main)
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Yifan Hou, Jiaoda Li, Yu Fei, Alessandro Stolfo, Wangchunshu Zhou, Guangtao Zeng, Antoine Bosselut, Mrinmaya Sachan
| Challenge: | Recent work has shown that language models (LMs) have strong multi-step (i.e., procedural) reasoning capabilities. |
| Approach: | They propose a mechanistic interpretation of language models for multi-step reasoning tasks by introducing a new probing approach that recovers the reasoning tree from the model’s attention patterns. |
| Outcome: | The proposed model implicitly embeds a reasoning tree resembling the correct reasoning process within it, and detects the information from the model’s attention patterns for most examples. |
Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026.findings-acl)
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Hanqi Jiang, Junhao Chen, Yi Pan, Ling Chen, Weihang You, Yifan Zhou, Ruidong Zhang, Yohannes Abate, Tianming Liu
| Challenge: | Large Language Models excel at generalized reasoning, but lack the ability to accumulate experiences and maintain narrative coherence over long horizons. |
| Approach: | They propose a unified memory architecture that transcends static vector similarity. |
| Outcome: | The proposed model outperforms state-of-the-art methods in temporal and multihop reasoning tasks. |
FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information (2022.emnlp-main)
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| Challenge: | FormLM is a pre-trained language model for creating semi-structured forms where questions and descriptions are organized by predefined structures. |
| Approach: | They propose to enhance pre-trained language model with form structural information to model online forms and recommend form creation ideas. |
| Outcome: | The proposed model outperforms general-purpose language models on all tasks, with an improvement by 4.71 on Question Recommendation and 10.6 on Block Type Suggestion in terms of ROUGE-1 and Macro-F1, respectively. |
SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation (2026.acl-long)
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Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu, Junyang Chen, Yanzhe Zhang, Shiwan Zhao, Jinyu Li, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin
| Challenge: | Existing methods for evaluating the perceptual quality of synthetic speech are limited due to the complexity of perceptual quality factors and the diversity of speech generation tasks. |
| Approach: | They propose a new paradigm for enabling large language models to conduct structured speech quality evaluation using a large-scale dataset. |
| Outcome: | The proposed model performs well across tasks and languages. |