Papers by Xi Fang
CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)
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Feiteng Fang, Liang Zhu, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks. |
| Approach: | They propose a Contrastive Learning Framework for Human Alignment to evaluate the noise within the data and dynamically adjust the training process. |
| Outcome: | The proposed framework surpasses other algorithms in terms of reward model scores, automatic evaluations, and human assessments on the widely used dataset "Helpful and Harmless" |
C-World: A Computer Use Agent Environment Creator (2026.acl-long)
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Ziqiao Xi, Shuang Liang, Qi Liu, Jiaqing Zhang, Letian Peng, Fang Nan, Meshal Nayim, Tianhui Zhang, Rishika Mundada, Lianhui Qin, Biwei Huang, Kun Zhou
| Challenge: | C-World enables users to build agent environments on demand. |
| Approach: | They propose a system that enables users to build agent environments on demand. |
| Outcome: | The proposed system outperforms baselines on 119k samples and achieves Spearman = 0.883 ranking correlation with real execution. |
TLSA: LLM-Guided Text-Label Space Alignment with Contrastive Learning for Generalized Category Discovery (2026.acl-long)
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| Challenge: | Existing methods for generalized category discovery suffer from weak text–label alignment, inconsistent objectives across known and novel categories, and poor discrimination of semantically similar clusters. |
| Approach: | They propose a unified framework that enforces contrastive alignment between text and label representations within a shared semantic space. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on four benchmark datasets. |
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. |
ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis (2026.acl-long)
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| Challenge: | High-Level Synthesis (HLS) is a hardware design tool that can be used to design hardware from C-like languages, but its widespread adoption is limited by strict coding constraints and design-specific optimizations. |
| Approach: | They propose a multi-agent HLS design framework that leverages specialized LLMs for automated debugging and directive tuning. |
| Outcome: | The proposed framework outperforms Gemini-3-pro in debugging and speedups across various HLS kernels and neural network accelerators. |
On-the-fly Cross-lingual Masking for Multilingual Pre-training (2023.acl-long)
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| Challenge: | In multilingual pre-training, multilingual models only learn cross-linguality implicitly from isomorphic spaces formed by overlapping different language spaces due to the lack of explicit cross-linguistic forward pass. |
| Approach: | They propose a dynamic token-wise masking scheme for multilingual pre-training that uses a special token [C]x to replace a random token in the input sentence. |
| Outcome: | The proposed model improves the performance of UNMT models on De, Ro, Ne En. |
OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking (2025.emnlp-main)
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Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable progress in machine writing such as open domain long-form generation. |
| Approach: | They propose a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection to improve the knowledge density of generated articles. |
| Outcome: | The proposed framework improves the knowledge density of generated articles without compromising metrics such as coherence and depth. |
Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)
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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni
| Challenge: | MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture. |
| Approach: | They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content. |
| Outcome: | The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context. |
WebWalker: Benchmarking LLMs in Web Traversal (2025.acl-long)
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Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. |
| Approach: | They propose a benchmark to assess the ability of LLMs to perform web traversal by using an explore-critic paradigm. |
| Outcome: | The proposed framework mimics human-like web navigation through an explore-critic paradigm and demonstrates the effectiveness of RAG combined with WebWalker in real-world scenarios. |
Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts (2025.emnlp-main)
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Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Chen Zeng Xi, Suncong Zheng, Xiaolong Liang, Xing Sun
| Challenge: | Recent models have extended Corresponding Author. context lengths to millions of tokens while maintaining reasoning and comprehension capabilities. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to extract sequential information items from long contexts. |
| Outcome: | The proposed model achieves maximum accuracy of 63.50% on six well-known LLMs. |
Leveraging Relaxed Equilibrium by Lazy Transition for Sequence Modeling (2022.acl-long)
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| Challenge: | Using attention-based models, certain tokens are less ambiguous than others, and they require fewer refinements for disambiguation. |
| Approach: | They propose a lazy transition mechanism to adjust the significance of iterative refinements for each token representation. |
| Outcome: | The proposed model outperforms baseline models on several tasks with the same number of parameters. |
SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)
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Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
| Challenge: | Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks. |
| Approach: | They propose a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search exploration to refine their action knowledge in the current environment. |
| Outcome: | The proposed framework synthesizes possible scenarios with multi-step action invocation within the action space and performs Monte Carlo Tree Search exploration to refine action knowledge in the current environment. |
Vocabulary-informed Language Encoding (2022.coling-1)
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| Challenge: | A Multilingual model relies on language encodings to identify input languages . a method to compute a vocabulary-informed language coding can improve multilingual models . |
| Approach: | They propose a method to compute a vocabulary-informed language encoding as the language representation for a required language. |
| Outcome: | The proposed method improves performance on unsupervised translation and cross-lingual embedding. |
The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs (2026.acl-short)
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| Challenge: | Using long-term memory, large language models can embed social hierarchies into their emotional reasoning. |
| Approach: | They evaluate 15 large language models on validated emotional intelligence tests to examine how user memory affects emotional intelligence. |
| Outcome: | The results show that the models with advantaged profiles receive more accurate emotional interpretations. |
Almost Free Semantic Draft for Neural Machine Translation (2021.naacl-main)
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| Challenge: | Empirical experiments show that the presented method can achieve competitive performance in common language pairs with a clear advantage in inference efficiency. |
| Approach: | They propose a method to sample and consider a semantic draft as global information from semantic space for decoding with almost free of cost. |
| Outcome: | Empirical results show that the proposed method can achieve competitive performance in common language pairs with a clear advantage in inference efficiency. |
Multilingual Pre-training with Self-supervision from Global Co-occurrence Information (2023.findings-acl)
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| Challenge: | Empirical studies show multilinguality and crosslinguality emerge from MLM pretraining without supervision. |
| Approach: | They propose to use global co-occurrence information as a source of structural information on multilingual corpora. |
| Outcome: | Empirical studies show that MLM-GC pre-training outperforms MLM pre- training for 4 downstream cross-lingual tasks and 1 additional monolingual task. |
Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs’ Hallucinations (2026.acl-long)
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| Challenge: | Existing methods to mitigate hallucinations include prompt engineering and model optimization, but lack domain generalization and potential errors in fine-tuning data may exacerbate the hallucism. |
| Approach: | They propose an expert-aware adaptive contrast decoding that uses expert differences in MoE’s higher layers to mitigate hallucinations on QA tasks. |
| Outcome: | The proposed method outperforms baseline models on four datasets Large language models (LLMs) show strong performance but suffer from hallucinations, limiting their application. |