Papers by Yifan Zheng
Graph Reasoning for Question Answering with Triplet Retrieval (2023.findings-acl)
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| Challenge: | Existing methods to answer complex questions require reasoning over knowledge graphs (KGs) state-of-the-art methods constrain retrieved knowledge in local subgraphs and discard more diverse triplets that are disconnected but useful for question answering. |
| Approach: | They propose a method to retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods on commonsenseQA and OpenbookQA datasets with 4.6% absolute accuracy. |
AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning (2024.emnlp-main)
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| Challenge: | Recent advances in memory-efficient zeroth-order methods have limited their widespread adoption due to performance drops and a high risk of divergence. |
| Approach: | They propose a memory-efficient zeroth-order framework to improve performance and convergence of the MeZO methods by using only forward passes. |
| Outcome: | The proposed framework improves performance and convergence of the proposed methods on Roberta-Large and Llama-2-7B models. |
Wanda++: Pruning Large Language Models via Regional Gradients (2025.findings-acl)
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Yifan Yang, Kai Zhen, Bhavana Ganesh, Aram Galstyan, Goeric Huybrechts, Markus Müller, Jonas M. Kübler, Rupak Vignesh Swaminathan, Athanasios Mouchtaris, Sravan Babu Bodapati, Nathan Susanj, Zheng Zhang, Jack FitzGerald, Abhishek Kumar
| Challenge: | Existing pruning methods suffer from accuracy degradation without full-model sparsity-aware fine-tuning. |
| Approach: | They propose a pruning framework that uses decoder-block-level regional gradients to improve pruning accuracy. |
| Outcome: | The proposed pruning framework outperforms the state-of-the-art pruning frameworks by utilizing decoder-block-level regional gradients. |
Aligning Large Language Models with Implicit Preferences from User-Generated Content (2025.acl-long)
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Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang
| Challenge: | Existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale. |
| Approach: | They propose a framework that leverages implicit preferences in unlabeled user-generated content to generate preference data. |
| Outcome: | The proposed framework transforms user-generated content into user queries and generates responses from the policy model. |
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)
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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
| Challenge: | Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability. |
| Approach: | They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs . |
| Outcome: | The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks. |
Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)
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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu
| Challenge: | Existing methods for AVE are limited on rare attributes due to poor generalization ability. |
| Approach: | They propose to leverage pretraining and transfer learning to address weaknesses in existing methods. |
| Outcome: | The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources. |
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. |
IHEval: Evaluating Language Models on Following the Instruction Hierarchy (2025.naacl-long)
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Zhihan Zhang, Shiyang Li, Zixuan Zhang, Xin Liu, Haoming Jiang, Xianfeng Tang, Yifan Gao, Zheng Li, Haodong Wang, Zhaoxuan Tan, Yichuan Li, Qingyu Yin, Bing Yin, Meng Jiang
| Challenge: | Instruction-tuned language models (LMs) are increasingly deployed as interactive services across various applications. |
| Approach: | They propose a benchmark to evaluate models' ability to follow the instruction hierarchy by comparing their models to a set of benchmarks. |
| Outcome: | The proposed benchmark covers 3,538 examples across nine tasks covering cases where instructions in different priorities either align or conflict. |
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)
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Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang
| Challenge: | Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. |
| Approach: | They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy. |
| Outcome: | The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy. |
MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models (2025.emnlp-main)
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Zhengyi Zhao, Shubo Zhang, Yuxi Zhang, Yanxi Zhao, Yifan Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu
| Challenge: | Current approaches focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context. |
| Approach: | They propose a benchmark to evaluate how large vision language models understand memes in their original context. |
| Outcome: | The proposed benchmark evaluates how large vision language models understand meme intent in their original context. |
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression (2026.findings-eacl)
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| Challenge: | Low-rank decomposition methods suffer from accuracy degradation and expensive calibration procedures. |
| Approach: | They propose a fast and accurate, training-free structural compression method based on fine-grained low-rank transformations in the activation space. |
| Outcome: | The proposed method outperforms pruning baselines in generalization and downstream performance while delivering inference speedups. |
Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)
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Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji
| Challenge: | Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge. |
| Approach: | They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities. |
| Outcome: | The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict. |
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2021.acl-long)
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Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, Yefeng Zheng
| Challenge: | Recent methods for extracting entities and relations from unstructured texts suffer from limitations, such as redundancy of relation prediction and inefficiency. |
| Approach: | They propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC) they propose overlapping triples for relation prediction and relation-relational alignment . |
| Outcome: | The proposed framework achieves state-of-the-art performance on public benchmarks with higher efficiency and consistent performance gain on complex scenarios of overlapping triples. |
Improving Consistency for Text Summarization with Energy Functions (2023.findings-emnlp)
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Qi Zeng, Qingyu Yin, Zheng Li, Yifan Gao, Sreyashi Nag, Zhengyang Wang, Bing Yin, Heng Ji, Chao Zhang
| Challenge: | Current abstractive summarization models generate inconsistent content due to the inherently noisy dataset and the discrepancy between maximum likelihood estimation based training objectives and consistency measurements. |
| Approach: | They propose a new consistency taxonomy that categorizes inconsistent content into faithfulness, factuality, and self-supportiveness. |
| Outcome: | Experiments on XSUM and CNN/DM datasets show that EnergySum mitigates the trade-off between accuracy and consistency. |
Rethinking Token Reduction for State Space Models (2024.emnlp-main)
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Zheng Zhan, Yushu Wu, Zhenglun Kong, Changdi Yang, Yifan Gong, Xuan Shen, Xue Lin, Pu Zhao, Yanzhi Wang
| Challenge: | Existing methods for token reduction for SSMs lead to performance drops . a recent study shows that Mamba-2 improves the accuracy of the model by 5.7% to 13.1% . |
| Approach: | They propose a token reduction method that integrates token importance and similarity into SSMs and takes advantage of pruning and merging. |
| Outcome: | The proposed method improves accuracy by 5.7% to 13.1% on six benchmarks with Mamba-2 compared to existing methods while reducing computational demands and memory requirements. |
Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models (2026.findings-acl)
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Bang Zhang, Ruotian Ma, Qingxuan Jiang, Peisong Wang, Jiaqi Chen, Zheng Xie, Xingyu Chen, Yue Wang, Fanghua Ye, Jian Li, Yifan Yang, Zhaopeng Tu, Xiaolong Li
| Challenge: | Large language models (LLMs) have evolved from statistical sequence predictors to sophisticated autonomous agents capable of reasoning, planning, and sustaining multi-turn conversa-tions. |
| Approach: | They propose a system that instantiates a "Sentient Agent" that simulates human-like emotional changes and inner thoughts to provide a more realistic evaluation of the model in multi-turn conversations. |
| Outcome: | The proposed framework measures the agent's higher-order social cognition in multi-turn conversations. |
Controlling Styles in Neural Machine Translation with Activation Prompt (2023.findings-acl)
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| Challenge: | Earlier studies on controlling styles in neural machine translation (NMT) have focused on regulating the level of formality, but they still encounter two major challenges. |
| Approach: | They propose a method to control the style of neural machine translation by retrieving prompts from stylized monolingual corpus. |
| Outcome: | The proposed method can control the style of translation and achieve remarkable performance. |
MuBench: Assessment of Multilingual Capabilities of Large Language Models Across 61 Languages (2026.findings-acl)
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| Challenge: | Existing evaluation datasets lack cross-lingual alignment, leaving assessments of multilingual capabilities fragmented in both language and skill coverage. |
| Approach: | They propose to use multilingual consistency as a complementary metric to assess performance bottlenecks and guide model improvement. |
| Outcome: | The proposed model lacks cross-lingual alignment and language coverage gaps between state-of-the-art 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. |
BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving (2025.acl-long)
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| Challenge: | Existing approaches to theorem proving in large language models rely on value functions and/or Monte Carlo Tree Search (MCTS), but the potential of simpler methods like Best-First Tree Search remains underexplored. |
| Approach: | They propose a scalable expert iteration framework that implements strategic data filtering at each expert iteration round, excluding problems solvable via beam search node expansion to focus on harder cases. |
| Outcome: | The proposed framework achieves a state-of-the-art score of 72.95 on the MiniF2F test set and challenges the perceived necessity of complex tree search methods. |
Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust Text-based Person Retrieval (2025.emnlp-main)
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| Challenge: | a large-scale visionlanguage pre-training framework is limited by the scarcity of large-sized annotated vision-language data . noise-resistant data construction pipeline is needed to filter and caption web-sourced images . noisy text tokens can be a problem for fine-grained representation learning . |
| Approach: | They develop a noise-resistant data construction pipeline that leverages in-context learning capabilities of MLLMs to automatically filter and caption web-sourced images. |
| Outcome: | The proposed framework improves cross-modal alignment by masking noisy textual tokens based on the gradient-attention similarity score. |
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel in translation and summarization due to the capabilities of transformer architectures. |
| Approach: | They propose to integrate tensorized adapters into model encoder/decoder blocks to improve model adaptability against data heterogeneity. |
| Outcome: | Experiments on large-scale cross-device FL and large-silo FL show that the proposed methods perform on par or even better than existing federated PEFT approaches while reducing communication cost. |
Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning (2026.acl-long)
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| Challenge: | Recent work on Chain-of-Thought prompting imposes substantial computational overhead . lack of supervision obscures the analyzability of the latent reasoning chain. |
| Approach: | They propose a framework to render latent reasoning chain into images, making latent rationale explicit and traceable. |
| Outcome: | The proposed framework achieves 3-4 token compression and substantial inference acceleration compared to explicit CoT prompting. |
MaZO: Masked Zeroth-Order Optimization for Multi-Task Fine-Tuning of Large Language Models (2025.emnlp-main)
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Zhen Zhang, Yifan Yang, Kai Zhen, Nathan Susanj, Athanasios Mouchtaris, Siegfried Kunzmann, Zheng Zhang
| Challenge: | Large language models (LLMs) have demonstrated exceptional capabilities across diverse tasks, but their fine-tuning requires significant memory, posing challenges for resource-constrained environments. |
| Approach: | They propose a ZO-based framework that eliminates the need for backpropagation and provides a memory-efficient alternative to backprograming. |
| Outcome: | The proposed framework surpasses first-order methods in performance and accuracy. |
SCOTT: Self-Consistent Chain-of-Thought Distillation (2023.acl-long)
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| Challenge: | Large language models (LMs) generate free-text rationales for their predictions via chain-of-thought prompting, but there is little guarantee that the generated rationale is consistent with LM’s predictions or faithfully justify the decisions. |
| Approach: | They propose a faithful knowledge distillation method to learn a small, self-consistent CoT model from a larger teacher model by contrastive decoding. |
| Outcome: | The proposed method yields comparable performance but is less faithful than baselines. |
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. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
| Challenge: | Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used. |
| Approach: | They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark. |
| Outcome: | The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history. |
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery (2023.findings-acl)
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Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin
| Challenge: | Existing intention-based studies on recommendation tasks are limited and use models to implicitly model the intention memberships. |
| Approach: | They propose a framework that leverages the generation power of large language models and human-in-the-loop annotation to semi-automatically construct the intention knowledge graph. |
| Outcome: | The proposed framework can model e-commerce knowledge and have many potential applications. |
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest (2022.coling-1)
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| Challenge: | Medical relation extraction (MRE) tasks aims to extract relations between entities in medical literature. |
| Approach: | They propose to combine semantic and syntactic information from medical texts by using causal explanation theory. |
| Outcome: | Empirically, the proposed model outperforms existing methods on benchmark medical datasets. |
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training (2022.findings-naacl)
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| Challenge: | Existing studies on keyphrase generation on non-English languages haven’t been vastly investigated. |
| Approach: | They propose a retrieval-augmented method for multilingual keyphrase generation that leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages. |
| Outcome: | The proposed model outperforms baselines on non-English keyphrase generation datasets and the proposed model is scalable. |
QUARTZ: Quantile-Aware Routing and Queueing for TTFT SLOs in LLM Serving (2026.findings-acl)
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| Challenge: | Prefill costs scale with prompt length and decode lengths are uncertain, and prefix locality creates strong performance skew across requests. |
| Approach: | They propose a quantile-aware routing and queueing layer that predicts conservative quantiles rather than point estimates using lightweight router-visible signals. |
| Outcome: | The proposed layer predicts conservative quantile-based request-cost proxies, rather than point estimates, using lightweight router-visible signals. |