Papers by He Xie
Can Pre-trained Language Models Interpret Similes as Smart as Human? (2022.acl-long)
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| Challenge: | Simile interpretation is a crucial task in natural language processing. |
| Approach: | They propose a task to let PLMs infer the shared properties of similes by probing textual corpora and human-designed questions. |
| Outcome: | The proposed task outperforms pre-trained language models on simile interpretation tasks while still underperforming humans. |
Automatic Model Selection with Large Language Models for Reasoning (2023.findings-emnlp)
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| Challenge: | Chain-of-Thought and Program-Aided Language Models offer different strengths and weaknesses. |
| Approach: | They propose a model selection method that uses a large language model to select between two different reasoning methods. |
| Outcome: | The proposed method shows significant performance improvements across eight reasoning datasets with Codex, ChatGPT, and GPT-4. |
Generative-to-Discriminative Test-Time Adaptation via Manifold-Aware Diffusion and Bayesian Distillation (2026.findings-acl)
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Boyun Zhang, Zequn Xie, Fangming Feng, Zihan Zhang, Yongbo He, Chuxin Wang, Sihang Cai, Tao Jin, Qifei Zhang
| Challenge: | Existing discriminative approaches suffer from "confident but wrong" failure mode, blindly adapting to OOD noise leading to error accumulation. |
| Approach: | They propose a TTA framework that harmonizes the robustness of generative diffusion models with the efficiency of discriminative regression networks via Bayesian Diffusion Distillation (BDD). |
| Outcome: | The proposed framework reduces MAE from 0.6872 to 0.5673 and boosts binary accuracy by 5.81 percentage points (reaching 57.33%) it also reduces the MAE of the MOSI to SIMS shift and achieves an 11.18-point gain over the baseline. |
FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining (2026.acl-long)
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| Challenge: | Existing audio-language models excel at clip-level understanding but struggle with frame-level tasks. |
| Approach: | They propose a novel training paradigm that advances both clip- and frame-level alignment in CLAP with heterogeneous data. |
| Outcome: | The proposed training paradigm improves both clip- and frame-level alignment in CLAP with heterogeneous data. |
ToolSafety: A Comprehensive Dataset for Enhancing Safety in LLM-Based Agent Tool Invocations (2025.emnlp-main)
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| Challenge: | Current models exhibit notable vulnerabilities in maintaining safety during multi-step tool interactions and in indirect harm scenarios. |
| Approach: | They propose a safety fine-tuning dataset to fine- tune LLMs into assistants . they propose to use synthesized trajectories and realistic, context-aware sample generation . |
| Outcome: | The proposed model maintains safety in multi-step and indirect harm scenarios with little impact on helpfulness. |
Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)
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Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He
| Challenge: | Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state. |
| Approach: | They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states . |
| Outcome: | The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets. |
DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale (2025.findings-acl)
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Linghao Zhang, Junhao Wang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Jiaheng Wen, Chengxing Xie, Maoquan Wang, Yufan Huang, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Existing studies highlight that dependency-related issues cause over 40% of observed runtime errors on the generated repository. |
| Approach: | They propose a large-scale benchmark and evaluation framework specifically designed to assess LLMs’ capability on dependency inference. |
| Outcome: | The proposed model achieves only a 48% execution pass rate on Python, indicating room for improvement. |
PodAgent: A Comprehensive Framework for Podcast Generation (2025.findings-acl)
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| Challenge: | Existing automatic audio generation methods struggle to generate podcast-like audio programs effectively. |
| Approach: | They propose a framework for creating podcast-like audio programs that generates informative topic-discussion content by designing a multi-agent collaboration system, builds a voice pool and uses LLM-enhanced speech synthesis to generate expressive conversational speech. |
| Outcome: | The proposed framework surpasses direct GPT-4 generation in topic-discussion dialogue content, and produces more expressive conversational speech. |
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)
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Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao
| Challenge: | Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions. |
| Approach: | They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models. |
| Outcome: | The proposed benchmarks show that the models perform better in open-ended conversations. |
InstructCoder: Instruction Tuning Large Language Models for Code Editing (2024.acl-srw)
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| Challenge: | InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. |
| Approach: | They propose to use Large Language Models to edit code based on user instructions . they use a dataset to adapt LLMs to general-purpose code editing . |
| Outcome: | The proposed model can significantly improve code editing performance compared to proprietary models . the proposed model is based on a human-written execution-based benchmark . |
Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation (2025.emnlp-main)
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Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray
| Challenge: | In the era of evaluating large language models, data contamination is an increasingly prominent concern . static benchmarking has been used for evaluation, but there are limitations of *dynamic* benchmarks . |
| Approach: | They propose a series of optimal design principles for *dynamic* benchmarking and analyze the limitations of existing *static* benchmarks. |
| Outcome: | The proposed benchmarks highlight a critical gap in the evaluation of LLMs. |
In-Context Former: Lightning-fast Compressing Context for Large Language Model (2024.findings-emnlp)
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| Challenge: | Existing methods to reduce inference costs of transformer-based large language models entail quadratic complexity . et al., 2017): transformer-derived large language model performance is a major challenge. |
| Approach: | They propose a method that compresses long contexts into short soft prompts . they use the self-attention mechanism of the large model to extract and condense information . |
| Outcome: | The proposed method reduces compression costs by 68 to 112 times while achieving 90% of baseline performance. |
PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning (2025.acl-long)
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| Challenge: | Recent studies have shown that LLMs can be significantly improved by integrating external tools. |
| Approach: | They propose a framework that integrates external tools into large language models to evaluate their ability to generate action plans. |
| Outcome: | The proposed framework evaluates the ability of large language models to generate action plans and generate action plan templates. |
SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution (2025.findings-acl)
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| Challenge: | Large Language Models excel in code generation benchmarks, but these benchmarks focus on single-file scenarios with constrained context scope. |
| Approach: | They propose an open-source framework to effectively resolve GitHub issues using a code file retrieval module and a model-based code editing module. |
| Outcome: | The proposed approach achieves state-of-the-art performance on two GitHub benchmarks. |
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion (2026.acl-long)
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| Challenge: | Existing quantization-based approaches to knowledge Graph Completion (KGC) are incomplete. |
| Approach: | They propose a framework that generates semantically coherent discrete codes for KG entities . they introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook . |
| Outcome: | The proposed framework outperforms existing text-based and embedding-based baselines in the KGC domain. |
Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored. |
| Approach: | They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods. |
| Outcome: | The proposed method significantly affects the internal states and token probability distributions of the LLM. |
INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent (2025.acl-long)
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Haohang Li, Yupeng Cao, Yangyang Yu, Shashidhar Reddy Javaji, Zhiyang Deng, Yueru He, Yuechen Jiang, Zining Zhu, K.p. Subbalakshmi, Jimin Huang, Lingfei Qian, Xueqing Peng, Jordan W. Suchow, Qianqian Xie
| Challenge: | Recent advances have underscored the potential of large language model (LLM)-based agents in financial decision-making. |
| Approach: | They propose to evaluate LLM agents using 13 different LLMs as backbone models across various market environments and tasks. |
| Outcome: | The proposed framework assesses the reasoning and decision-making capabilities of 13 different LLMs across various market environments and tasks. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
Industry Scale Semi-Supervised Learning for Natural Language Understanding (2021.naacl-industry)
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| Challenge: | Obtaining human annotation is expensive and time-consuming process. |
| Approach: | They propose a semi-supervised learning pipeline which leverages millions of unlabeled examples to improve natural language understanding tasks. |
| Outcome: | The proposed pipeline can be used to improve natural language understanding tasks. |
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
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Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
| Approach: | They propose a method to optimize prompts for in-context learning by a generator and a discriminator. |
| Outcome: | The proposed method improves state-of-the-art prompt optimization techniques on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. |
CAST: Achieving Stable LLM-based Text Analysis for Data Analytics (2026.findings-acl)
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| Challenge: | Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labeling. |
| Approach: | They propose a framework that enhances output stability by constraining the model’s latent reasoning trajectory. |
| Outcome: | The proposed framework improves stability by constraining the model's latent reasoning trajectory. |
Towards Visual Question Answering on Pathology Images (2021.acl-short)
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| Challenge: | Pathology imaging is used for identifying the causes and effects of diseases or injuries. |
| Approach: | They propose a pathological visual question answering framework to analyze pathology images and answer medical questions related to these images. |
| Outcome: | The proposed framework performs self-supervised pretraining and finetuning end-to-end to learn powerful visual and textual representations jointly and automatically identifies and excludes noisy self-controlled examples from pretraining. |
How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior (2026.acl-long)
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Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He, Zirui Liu, Jiliang Tang, Himabindu Lakkaraju, Zhen Xiang
| Challenge: | In practice, memory designs vary widely across agents due to their diverse objectives and functionalities. |
| Approach: | They conduct an empirical study on how memory management choices impact the LLM agents’ behavior, especially their long-term performance. |
| Outcome: | The proposed methods show that LLM agents display an experience-following property, which results in highly similar agent outputs. |
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)
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| Challenge: | Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context. |
| Approach: | They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals. |
| Outcome: | The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness. |
MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus (2026.findings-acl)
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Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Multimodal Large Language Models (MLLMs) have advanced Chinese Classical Studies (CCS) but the audio dimension of CCS remains underexplored due to a lack of high-quality, domain-specific audio corpora. |
| Approach: | They propose a 119-hour audio corpus comprising 22,000 audio samples to bridge this gap . it encompasses a diverse range of literary genres across six tasks . |
| Outcome: | The proposed corpus encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering ( SQA), Speech Understanding (SU), and Speech Reasoning (SR). |
A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation (2022.findings-acl)
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Tianxiang Sun, Xiangyang Liu, Wei Zhu, Zhichao Geng, Lingling Wu, Yilong He, Yuan Ni, Guotong Xie, Xuanjing Huang, Xipeng Qiu
| Challenge: | Existing methods to measure instance difficulty use generalization and threshold-tuning . a new approach to learn to exit is based on hash functions to assign tokens to a fixed exiting layer. |
| Approach: | They propose a Hash-based Early Exiting approach that replaces learn-to-exit modules with hash functions to assign each token to a fixed exiting layer. |
| Outcome: | The proposed approach improves on learning to exit and predicting instance difficulty. |
Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data (2026.acl-long)
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Yuxuan Lu, Jing Huang, Yan Han, Bingsheng Yao, Sisong Bei, Yaochen Xie, Yisi Sang, Qi He, Dakuo Wang
| Challenge: | Recent research shows that LLM Agents can generate “believable” human behaviors via prompt-only methods, leaving open questions of whether they can accurately generate step-by-step actions in multi-turn interaction tasks. |
| Approach: | They propose to use shopping data to evaluate LLMs' ability to accurately generate step-by-step actions in a multi-turn interaction task. |
| Outcome: | The proposed model achieves 17.26% action generation accuracy and 33.86% F1 score on final purchase prediction, representing improvements of 5.4% and 13.85% over baselines. |
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond (2022.coling-1)
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| Challenge: | Existing models for dialogue summarization focus on document summarizing on time and speaker-centered points, but this approach is limited in understanding the dialogue. |
| Approach: | They propose a 2D view of dialogue based on a time-speaker perspective where the time and speaker streams of dialogue can be obtained as strengthened input. |
| Outcome: | The proposed model outperforms existing models on the QMSum dataset and improves summary faithfulness and human evaluation. |
FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)
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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie
| Challenge: | FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions . |
| Approach: | They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures . |
| Outcome: | The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes. |
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023.emnlp-industry)
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Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie
| Challenge: | Existing typography solutions lack adaptability, creativity, and computational efficiency. |
| Approach: | They propose a user-driven framework for artistic typography synthesis based on the Large Language Model (LLM) the LLM Engine interprets user inputs and generates actionable prompts for the other modules, transforming abstract concepts into tangible designs. |
| Outcome: | The proposed framework incorporates four key modules: the LLM Engine, SemTypo, StyTyPo, and TexTyPO. |
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)
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Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He
| Challenge: | Retrieval-augmented generation (RAG) enhances the question answering abilities of large language models (LLMs) however, adapting general-purpose RAG systems to specialized fields poses unique challenges due to distribution shifts and limited access to domain-specific data. |
| Approach: | They propose a method that equips large language models with joint capabilities of question answering and question generation for domain adaptation. |
| Outcome: | Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2%–8.6%. |
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. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
RD-MCSA: A Multi-Class Sentiment Analysis Approach Integrating In-Context Classification Rationales and Demonstrations (2025.emnlp-main)
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| Challenge: | Existing methods for multi-class sentiment analysis (MCSA) are difficult due to subtle semantic differences between adjacent sentiment levels and the scarcity of high-quality annotated data. |
| Approach: | They propose a framework to integrate classification rationales with adaptively selected demonstrations to enhance MCSA performance under limited supervision. |
| Outcome: | The proposed framework outperforms baseline and standard ICL methods on five benchmark datasets. |
UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models (2025.findings-naacl)
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Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, Benyou Wang
| Challenge: | Recent advances in large language models (LLMs) have expanded their potential applications in finance. |
| Approach: | They propose a framework to evaluate the ability of large language models to handle financial tasks using human expert evaluations and task-specific interactions. |
| Outcome: | The proposed framework evaluates the ability of large language models to handle complex financial tasks and combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. |
Personalized Abstractive Summarization by Tri-agent Generation Pipeline (2024.findings-eacl)
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| Challenge: | Existing research shows that large language models do not consistently satisfy users' preferences or expectations. |
| Approach: | They propose a tri-agent generation pipeline that includes a generator, an instructor, and an editor to enhance output personalization. |
| Outcome: | The proposed pipeline generates outputs that better meet user expectations on two abstractive summarization datasets. |
An Empirical Analysis of Leveraging Knowledge for Low-Resource Task-Oriented Semantic Parsing (2023.findings-acl)
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Mayank Kulkarni, Aoxiao Zhong, Nicolas Guenon des mesnards, Sahar Movaghati, Mukund Sridhar, He Xie, Jianhua Lu
| Challenge: | Task-oriented semantic parsing is a new approach to represent the meaning of user requests with arbitrarily nested semantics. |
| Approach: | They propose to use knowledge-enhanced encoders to parse user requests with arbitrarily nested semantics. |
| Outcome: | The proposed model improves performance in low-resource and low-compute settings. |
Automatic Song Translation for Tonal Languages (2022.findings-acl)
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| Challenge: | Existing automatic song translation systems for tonal languages do not match the number of notes and beat the original rhythm of the song. |
| Approach: | They propose three criteria for effective AST: preserving meaning, singability and intelligibility. |
| Outcome: | The proposed system balances semantics and singability with human evaluations. |
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. |
Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models (2026.findings-acl)
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He Xiao, Qingyao Yang, Dirui Xie, Wendong XU, Zunhai Su, Runming Yang, Haobo Liu, Wenyong Zhou, Zhengwu Liu, Ngai Wong
| Challenge: | Large language models with billions of parameters are often over-provisioned . smaller models exhibit lower robustness under extreme low-bit quantization . |
| Approach: | They propose a hardware-native, metric-driven post-training quantization framework that keeps uniform bit-width within each layer while mixing precision across layers. |
| Outcome: | LieQ reduces large accuracy gap observed for large language models with billions of parameters while preserving standard multiplication kernels. |
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services (2025.emnlp-industry)
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Fei Zhao, Chonggang Lu, null Wangyue, Zheyong Xie, Ziyan Liu, Haofu Qian, Jianzhao Huang, Fangcheng Shi, Zijie Meng, Hongcheng Guo, Mingqian He, Xinze Lyu, Zheyu Ye, Weiting Liu, Boyang Wang, Shaosheng Cao
| Challenge: | Social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. |
| Approach: | They propose a domain-specific LLM to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for social networking services. |
| Outcome: | The proposed model achieves an average improvement of 14.02% across 8 major tasks and 7.56% in bilingual evaluation benchmark, compared with baseline models. |
PENS: A Dataset and Generic Framework for Personalized News Headline Generation (2021.acl-long)
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| Challenge: | Using a dataset of Microsoft News, we propose a generic framework to personalize a text generator and establish personalized headlines. |
| Approach: | They propose a generic framework to personalize a news headline generator and establish personalized headlines by leveraging user behavioral data. |
| Outcome: | The proposed framework is based on user preference data and user preference injections to personalize a text generator and establish personalized headlines. |
Towards Realistic Single-Task Continuous Learning Research for NER (2021.findings-emnlp)
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Justin Payan, Yuval Merhav, He Xie, Satyapriya Krishna, Anil Ramakrishna, Mukund Sridhar, Rahul Gupta
| Challenge: | Academic datasets are often static and contain data that is annotated all at once based on fixed annotation guidelines. |
| Approach: | They propose to build a single-task continuous learning dataset from an existing dataset and release it along with the code to the research community. |
| Outcome: | The proposed model is based on an existing dataset and released to the research community. |
EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)
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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao
| Challenge: | Existing benchmarks focus on well-defined or abstract reasoning and fail to capture real-world engineering problems. |
| Approach: | They propose a hierarchical benchmark to evaluate large language models on engineering problems. |
| Outcome: | The proposed model performs well under well-defined conditions and is based on three levels of difficulty and covers diverse engineering subfields. |
UCS-SQL: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to-SQL (2025.findings-acl)
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Zhenhe Wu, Zhongqiu Li, JieZhangChinaTele JieZhangChinaTele, Zhongjiang He, Jian Yang, Yu Zhao, Ruiyu Fang, Bing Wang, Hongyan Xie, Shuangyong Song, Zhoujun Li
| Challenge: | Existing methods overlook the challenge of effectively transforming structure information from NL to SQL. |
| Approach: | They propose a text-to-SQL framework that unites content and structure pipes to bridge the gap between NL and SQL. |
| Outcome: | The proposed framework bridges the gap between natural language questions and SQL by combining content and structure pipes. |
Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning (2026.findings-acl)
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| Challenge: | a framework to mitigate spurious optimization signals is proposed for test-time reinforcement learning (TTRL) Reinforcement learning with verifiable rewards (RLVR) is an effective paradigm for improving large language models on structured challenging reasoning tasks. |
| Approach: | They propose a framework to mitigate spurious optimization signals from label noise . they propose to use a frequency-based sampling strategy to exclude ambiguous samples . |
| Outcome: | The proposed framework outperforms existing TTRL baselines on three large language models across multiple mathematical reasoning benchmarks. |
HookMoE: A learnable performance compensation strategy of Mixture-of-Experts for LLM inference acceleration (2025.emnlp-main)
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| Challenge: | Mixture of Experts (MoE) models have been a promising paradigm for scaling model capacity through top-k routing mechanisms. |
| Approach: | They propose a plug-and-play single-layer compensation framework that strategically inserts a lightweight trainable Hook module immediately preceding selected transformer blocks. |
| Outcome: | The proposed framework reduces the number of activated experts by more than 50% and achieves a 1.42 inference speed-up during the prefill stage. |
INT: Establishing Information Transfer for Multilingual Intent Detection and Slot Filling (2025.findings-acl)
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Di Wu, Liting Jiang, Bohui Mao, Hongyan Xie, Haoxiang Su, Zhongjiang He, Ruiyu Fang, Shuangyong Song, Hao Huang, Xuelong Li
| Challenge: | Existing studies struggle to achieve performance comparable to that on high-resource languages due to inherent linguistic diversity of multilingual SLU tasks. |
| Approach: | They propose a multilingual information transfer network to solve these challenges . they propose to reformulate SF as a span prediction problem and introduce a slot-matching attention mechanism to achieve slot alignment across languages. |
| Outcome: | The proposed model outperforms baseline models on the MASSIVE and MASSIV-UG datasets in overall accuracy across all languages. |
APEX: Learning Adaptive Priorities for Multi-Objective Alignment in Vision-Language Generation (2026.findings-acl)
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Dongliang Chen, Xinlin Zhuang, Junjie Xu, Luojian Xie, Zehui Wang, Jiaxi Zhuang, Haolin Yang, Liang Dou, Xiao He, Xingjiao Wu, Ying Qian
| Challenge: | APEX optimizes for text-to-image generation by combining learning potential, conflict penalty, and progress need. |
| Approach: | They propose an algorithm that stabilizes heterogeneous rewards and dynamically schedules objectives . they propose a method that achieves better Pareto trade-offs across four heterogenous objectives based on P3 Adaptive Priorities . |
| Outcome: | The proposed algorithm achieves better pareto trade-offs across four heterogeneous objectives while maintaining competitive OCR accuracy. |
Measuring and Mitigating Local Instability in Deep Neural Networks (2023.findings-acl)
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| Challenge: | Uncertain details like random initialization can change the outputs of a trained system with potentially disastrous consequences. |
| Approach: | They propose a model stability problem by studying how the predictions of a deep neural network change as a consequence of stochasticity in the training process. |
| Outcome: | The proposed method outperforms data-agnostic methods and is 90% cheaper than the gold standard. |
Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning (2025.findings-acl)
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Haoyu Han, Yaochen Xie, Hui Liu, Xianfeng Tang, Sreyashi Nag, William Headden, Yang Li, Chen Luo, Shuiwang Ji, Qi He, Jiliang Tang
| Challenge: | Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks, however, they still face challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences. |
| Approach: | They propose to construct explicit graphs from context and leverage them to enhance LLM reasoning performance on reasoning tasks. |
| Outcome: | Extensive experiments show that the proposed method improves both logical reasoning and multi-hop question answering tasks. |
MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval (2024.findings-naacl)
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| Challenge: | Large-scale visual-language pretraining models have shown remarkable capabilities in understanding both vision and language. |
| Approach: | They propose a multi-teacher cross-modality alignment distillation technique to integrate the advantages of single-stream and dual-stream models. |
| Outcome: | The proposed model is lightweight and has only 100M running memory and 8.0ms search latency. |
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation (2021.acl-demo)
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Junyi Li, Tianyi Tang, Gaole He, Jinhao Jiang, Xiaoxuan Hu, Puzhao Xie, Zhipeng Chen, Zhuohao Yu, Wayne Xin Zhao, Ji-Rong Wen
| Challenge: | TextBox is an open-source text generation framework that is modularized and extensible. |
| Approach: | They propose to provide a unified, modularized, and extensible text generation framework that implements 21 text generation models on 9 benchmark datasets. |
| Outcome: | The proposed framework implements 21 models on 9 benchmark datasets and is available under the Apache License 2.0 license. |
CoDA: Restoring Contextual Dominance via Copy-Encouraged Attention Intervention for Mitigating RAG Hallucinations (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation reduces hallucination by grounding outputs in external evidence. |
| Approach: | They propose a lightweight inference-time attention intervention that amplifies evidence-aligned value states to enhance contextual faithfulness and reduce hallucination. |
| Outcome: | The proposed model reduces hallucination by grounding model outputs in external evidence. |