Papers by Haoran Liu

DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog (2023.findings-emnlp)

Challenge:	Existing techniques for extending context capabilities in LLMs require additional training procedures and access to datasets with long context (e.g., sequences of 32K tokens).
Approach:	They propose a solution to extend context capabilities in Large Language Models by training a single process over a sequence of 4K tokens.
Outcome:	The proposed solution significantly reduces the cost of continual-pretraining or fine-tuning over short sequences and improves robustness to diverse relative positions.

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Xin Zheng, Tianyu Liu, Haoran Meng, Xu Wang, Yufan Jiang, Mengliang Rao, Binghuai Lin, Yunbo Cao, Zhifang Sui

Natural Language Interfaces

Challenge:	Existing work on question-answer extraction fails to integrate incomplete utterances from dialog context for composite QA retrieval.
Approach:	They propose a task where questions and corresponding answers might be separated across different utterances.
Outcome:	The proposed methods perform well on 5 customer service datasets and set a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics.

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)

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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin

Challenge:	a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities .
Approach:	They present a comparative analysis to identify and distinguish LLM activities from human activities.
Outcome:	The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities.

Guided Knowledge Generation with Language Models for Commonsense Reasoning (2024.findings-emnlp)

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Xiao Wei, Haoran Chen, Hang Yu, Hao Fei, Qian Liu

Reasoning

Challenge:	Large Language Models (LLMs) have achieved notable success in commonsense reasoning tasks, benefiting from extensive world knowledge acquired through extensive pretraining.
Approach:	They propose a method to generate knowledge explanations and to automatically assign labels based on the probability of correct answers.
Outcome:	The proposed method outperforms baselines on four widely-used commonsense reasoning benchmarks and shows that it can generate high quality knowledge leading to correct answers.

One Cognitive Loop Is Enough: SODA unlocks Pure-Text Spatial Reasoning in Large Language Models (2026.acl-long)

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Shunwen Bai, Jiahuan Zhang, Haoran Huang, Yurun Wang, Jiale Liu, Yanxi Wu, Ningzhe Yu, Yudong Gao, Mingjun Cheng

Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning (2026.findings-acl)

Challenge:	Existing large language models (LLMs) lack visual input, leading to errors in basic numerical comparisons.
Approach:	They propose a spatial OODA framework that integrates the OODAC cognitive loop into multiple control tasks and integrates it into LLMs.
Outcome:	The proposed model significantly improves the spatial reasoning capabilities of large language models across multiple scenarios including SPOD-Bench, SPACE and applications.

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Wenjin Liu, Haoran Luo, Xueyuan Lin, Haoming Liu, Tiesunlong Shen, Jiapu Wang, Rui Mao, Erik Cambria

Low-Resource NLP

Challenge:	Existing large language models are limited in understanding, reasoning, calculation, and generation, limiting their performance in complex reasoning and dynamic tasks.
Approach:	They propose a plug-and-play framework that integrates a small-scale LLM (as agent) with large-scale large-level LLMs (a as environment) they propose generating prompts that are used to interact with LLM, and a double constraint reward that optimizes correctness and quality of generation.
Outcome:	The proposed framework significantly outperforms baseline large-scale large-language models across various tasks.

TopicAttack: An Indirect Prompt Injection Attack via Topic Transition (2025.emnlp-main)

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Yulin Chen, Haoran Li, Yuexin Li, Yue Liu, Yangqiu Song, Bryan Hooi

A Survey on LLMs for Story Generation (2025.findings-emnlp)

Challenge:	Recent attacks have demonstrated potential, but their abrupt instruction injection often undermines their effectiveness.
Approach:	They propose a method that prompts the LLM to generate a fabricated conversational transition prompt that gradually shifts the topic toward the injected instruction.
Outcome:	The proposed method achieves state-of-the-art performance with an attack success rate (ASR) over 90% in most cases, even when various defense methods are applied.

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Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee

Text Generation

Challenge:	Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently.
Approach:	They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation .
Outcome:	The proposed taxonomy compares existing work on the topic with those of novel author-assistance models.

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)

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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song

Reasoning

Challenge:	Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning.
Approach:	They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized.
Outcome:	The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized .

MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection (2025.acl-long)

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Ziyan Liu, Chunxiao Fan, Haoran Lou, Yuexin Wu, Kaiwei Deng

Efficient Hybrid Generation Framework for Aspect-Based Sentiment Analysis (2023.eacl-main)

Challenge:	a rapid expansion of memes on social media highlights the need for effective methods to detect harmful content.
Approach:	They propose a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data.
Outcome:	The proposed framework outperforms existing zero-shot approaches on three meme datasets.

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Haoran Lv, Junyi Liu, Henan Wang, Yaoming Wang, Jixiang Luo, Yaxiao Liu

Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)

Challenge:	Aspect-based sentiment analysis (ABSA) has attracted broad commercial attention due to its commercial value.
Approach:	They propose a framework that generates location and semantic information in parallel and a global hybrid loss function in combination with bipartite matching to achieve end-to-end model training.
Outcome:	The proposed framework outperforms state-of-the-art methods in almost all cases and outperfies existing methods in terms of inference efficiency.

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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing

Text Generation

Challenge:	Texar is an open-source text generation toolkit that supports a broad set of text generation tasks.
Approach:	They introduce Texar, an open-source text generation toolkit that supports text generation tasks.
Outcome:	Texar supports machine translation, summarization, dialog, content manipulation, and more.

Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction (2026.findings-acl)

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Yulin Chen, Haoran Li, Yuan Sui, Yue Liu, Yufei He, Xiaoling Bai, Chi Fei, Li Yabo, Haozhe Ma, Yangqiu Song, Bryan Hooi

Can Indirect Prompt Injection Attacks Be Detected and Removed? (2025.acl-long)

Challenge:	Prompt injection attacks manipulate large language models (LLMs) by misleading them to deviate from the original input instructions and execute maliciously injected instructions.
Approach:	They propose a prompt injection defense method that suppresses the model's instruction-following tendencies rather than suppressing them.
Outcome:	The proposed method outperforms prompt-engineering-based approaches and fine-tuning methods and reduces the ASR to nearly 0% in some scenarios.

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Yulin Chen, Haoran Li, Yuan Sui, Yufei He, Yue Liu, Yangqiu Song, Bryan Hooi

Tuna: Instruction Tuning using Feedback from Large Language Models (2023.findings-emnlp)

Challenge:	Recent studies have developed various detection mechanisms to protect against prompt injection attacks.
Approach:	They investigate the feasibility of detecting and removing indirect prompt injection attacks . they use two methods to evaluate their performance and train detection models .
Outcome:	The proposed method is based on a benchmark dataset and is available on github . it evaluates the performance of existing models and open-source detection models .

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Haoran Li, Yiran Liu, Xingxing Zhang, Wei Lu, Furu Wei

LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety (2026.acl-long)

Challenge:	LLms like LLaMA have shown to be cost-effective for generating better responses . however, the instruction-tuned model has only seen one response per instruction .
Approach:	They propose to fine tune an instruction-tuned LLM using probabilistic ranking and contextual ranking approaches to increase the likelihood of generating better responses.
Outcome:	The proposed model improves on Super Natural Instructions, LMentry and Vicuna QA.

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Junxiao Yang, Haoran Liu, Jinzhe Tu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Jiaqi Weng, Jialing Tao, Hui Xue, Hongning Wang, Han Qiu, Minlie Huang

Challenge:	Large language models (LLMs) have demonstrated better safety performance in high-resource languages than in low-resourced languages.
Approach:	They propose language-agnostic semantic alignment (LASA) which anchors safety alignment directly in semantic bottlenecks.
Outcome:	The proposed approach significantly improves safety across all languages: average attack success rate drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains within 3–4% across Qwen2.5 and Qwend3 Instruct models (7B–32B).

Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks (2023.findings-emnlp)

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Zixuan Ke, Bing Liu, Wenhan Xiong, Asli Celikyilmaz, Haoran Li

DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection (2022.findings-emnlp)

Challenge:	Existing methods for Continual Learning (CL) have limited KT and catastrophic forgetting . a new method overcomes CF by isolating the knowledge of each task .
Approach:	They propose a method to overcome catastrophic forgetting and encourage knowledge transfer . they propose to discover a sub-network for each task and a soft-masking mechanism to preserve the previous knowledge.
Outcome:	The proposed method outperforms baselines in classification, generation, information extraction and their mixture.

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Haoran Meng, Zheng Xin, Tianyu Liu, Zizhen Wang, He Feng, Binghuai Lin, Xuemin Zhao, Yunbo Cao, Zhifang Sui

Natural Language Interfaces

Challenge:	DialogUSR is a plug-in and domain-agnostic module that empowers multi-intent detection for chatbots . a single user query triggers inquiries on highspeed train ticket price and weather of destination.
Approach:	They propose a dialog utterance splitting and reformulation task that splits multi-intent user query into multiple single-intention sub-queries and recovers all coreferred and omitted information in the sub-questions.
Outcome:	The proposed model can be used to split multi-intent user queries into multiple sub-queries . it can be trained in two stages and perform in-depth analyses on the proposed models .

ProgressLM: Towards Progress Reasoning in Vision-Language Models (2026.acl-long)

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Jianshu Zhang, Chengxuan Qian, Haosen Sun, Haoran Lu, Dingcheng Wang, Letian Xue, Han Liu

K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)

Challenge:	Existing models for task progress estimation lack long-horizon and dynamic reasoning . estimating how much of a task has been completed requires long-term reasoning based on partial information.
Approach:	They propose a benchmark for evaluating progress reasoning from a single observation . they instantiate a two-stage paradigm that combines episodic retrieval with mental simulation .
Outcome:	The proposed benchmark improves on 14 VLMs on a small scale and shows common failure patterns.

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Song Xu, Haoran Li, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Ying Liu, Bowen Zhou

Learning Invariant Representation Improves Robustness for MRC Models (2022.findings-emnlp)

Challenge:	Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios.
Approach:	They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks.
Outcome:	The proposed model significantly outperforms baselines across the board in e-commerce scenarios.

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Yu Hai, Liang Wen, Haoran Meng, Tianyu Liu, Houfeng Wang

Robustness in NLP

Challenge:	Existing approaches to improve machine reading comprehension models are vulnerable and not robust to adversarial examples.
Approach:	They propose to construct positive example pairs which have same answer by augmentation and then introduce stability and contrastive loss to improve invariance of representation.
Outcome:	The proposed approach boosts the robustness of QA models across different tasks and attack sets significantly and consistently.

GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)

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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li

Structured Data in NLP

Challenge:	Existing fact-checking methods that use large language models often generate subtle factual errors.
Approach:	They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation.
Outcome:	GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call.

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

Bypassing Neural Evaluations for Fast Audio Editing via Adaptive Trajectory Extrapolation (2026.findings-acl)

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Xiaoqian Liu, Zhengkun Ge, Jianjin Wang, Haoran Zhang, Yuan Ge, Kaiyan Chang, Chen Xu, Tong Xiao, Zhengtao Yu, Linfeng Zhang, JingBo Zhu

Multimodality

Challenge:	Recent advances in audio diffusion models have significantly improved text-to-audio editing via inversion techniques, but these models typically rely on dense, fixed-step sampling trajectories to maintain structural integrity.
Approach:	They propose a model-agnostic Adaptive Trajectory Extrapolation framework that accelerates inversion-based editing process by dynamically evaluating only the most critical generative phases.
Outcome:	The proposed framework achieves a 3.9 speedup with negligible loss in fidelity.

MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)

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Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu

NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding (2024.findings-emnlp)

Challenge:	Existing methods for optimizing reasoning quality are limited by overthinking.
Approach:	They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time.
Outcome:	The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%.

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Chunkit Chan, Cheng Jiayang, Yauwai Yim, Zheye Deng, Wei Fan, Haoran Li, Xin Liu, Hongming Zhang, Weiqi Wang, Yangqiu Song

Language Constrained Multimodal Hyper Adapter For Many-to-Many Multimodal Summarization (2025.acl-long)

Challenge:	Theory of mind evaluations currently focus on testing models using machine-generated data or game settings prone to shortcuts and spurious correlations.
Approach:	They propose a benchmark to stress-test machine ToM in real-world negotiation surrounding covered multi-dimensional mental states.
Outcome:	The proposed benchmark builds upon the Belief-Desire-Intention theory and conducts the necessary empirical experiments to evaluate large language models.

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Nayu Liu, Fanglong Yao, Haoran Luo, Yong Yang, Chen Tang, Bo Lv

Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations (2025.findings-naacl)

Challenge:	Existing models that share parameters neglect the language-specific knowledge learning.
Approach:	They propose a language-constrained multimodal hyper adapter for multimodal summarization that integrates language-specific adapters into multilingual pre-trained backbones.
Outcome:	The proposed model can generate summaries based on multimodal documents such as text and visuals, allowing people to quickly locate key information from the vast multimedia con.

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Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang, Yiran Wei, Wai Lam

Dialogue Systems & Conversational Agents

Challenge:	a new paradigm for dialogue systems is being developed to mimic human interactions . the current single-step dialogue paradigm lacks the depth and fluidity of human interactions.
Approach:	They propose a step-by-step dialogue paradigm that mimics human interactions . they use a dataset to fine-tune existing language models .
Outcome:	The proposed system mimics the dynamic nature of human conversations . it is compared with existing paradigms and will be released later this year .

Meet Changes with Constancy: Learning Invariance in Multi-Source Translation (2020.coling-main)

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Jianfeng Liu, Ling Luo, Xiang Ao, Yan Song, Haoran Xu, Jian Ye

Machine Translation

Challenge:	Existing approaches to multi-source neural machine translation neglect inconsistencies between sources of information.
Approach:	They propose a source invariance network to learn invariant information of parallel sources . they propose to integrate such network with multi-encoder based multi-source NMT methods .
Outcome:	The proposed approach achieves clear gains in translation quality and captures implicit invariance between different sources.

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation (2026.acl-long)

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Peiru Yang, Haoran Zheng, Tong Ju, Shiting Wang, Wanchun Ni, Jiajun Liu, Shangguang Wang, Yongfeng Huang, Tao Qi

From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents (2026.findings-acl)

Challenge:	Existing studies have investigated knowledge poisoning attacks in medical RAG systems . knowledge poison attacks can disrupt model outputs and undermine system reliability .
Approach:	They propose a knowledge poisoning framework that injects misinformation into textual data . they propose to use paired visual data as a query-agnostic trigger to promote retrieval .
Outcome:	The proposed framework produces clinically plausible but incorrect generations on five LLMs and datasets.

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Haoran Tan, Zeyu Zhang, Chen Ma, Tianze Liu, Quanyu Dai, Xu Chen

Challenge:	Existing plans for large language model-based agents are limited by their granularity and lack flexibility.
Approach:	They propose a self-adaptive hierarchical planning mechanism that mimics human planning strategies and generates self-adapted hierarchic plans tailored to the varying difficulty levels of different tasks.
Outcome:	The proposed method significantly improves task execution success rates while mitigating overthinking at the planning level, providing a flexible and efficient solution for multi-step complex decision-making tasks.

Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues (2022.acl-long)

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Qingxiu Dong, Ziwei Qin, Heming Xia, Tian Feng, Shoujie Tong, Haoran Meng, Lin Xu, Zhongyu Wei, Weidong Zhan, Baobao Chang, Sujian Li, Tianyu Liu, Zhifang Sui

Multimodality

Challenge:	Existing work in vision language cross-modal reasoning uses binary or multi-choice classification based on source image and textual query.
Approach:	They propose a task where a textual premise is the background presumption on each source image.
Outcome:	The proposed task is based on a dataset of 15,360 movie screenshots and human-curated premise templates from 6 pre-defined categories.

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)

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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang

STSPL-SSC: Semi-Supervised Few-Shot Short Text Clustering with Semantic text similarity Optimized Pseudo-Labels (2024.findings-acl)

Challenge:	Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases.
Approach:	They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
Outcome:	The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs.

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Wenhua Nie, Lin Deng, Chang-Bo Liu, JialingWei JialingWei, Ruitong Han, Haoran Zheng

Information Extraction & Text Mining

Challenge:	Existing methods for obtaining task-specific labels require prior knowledge of clustering categories and uncontrollable clustering centers.
Approach:	They propose a framework for supervised clustering using a discrete process and a robust Contrastive Learning module.
Outcome:	The proposed framework outperforms state-of-the-art models on a real-world dataset with just one label per class . the proposed framework is based on k-means clustering and a robust Contrastive Learning module .

MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)

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Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang

Multimodality

Challenge:	Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences.
Approach:	They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
Outcome:	The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner.

Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation (2020.coling-main)

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Na Liu, Xiangdong Su, Haoran Zhang, Guanglai Gao, Feilong Bao

Syntactic Text Processing

Challenge:	Mongolian morphological segmentation is a crucial preprocessing step in many Mongolian related NLP applications.
Approach:	They propose a neural network incorporating inner-word and out-word features for Mongolian morphological segmentation.
Outcome:	The proposed network is compared with baselines and evaluates its performance.

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026.acl-long)

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

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Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Sahel Sharifymoghaddam, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Hosna Oyarhoseini, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin

InteGround: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding (2025.findings-emnlp)

Challenge:	Existing benchmarks for deep search agents rely on blackbox web search APIs . dynamic and opaque web APIs hinder reproducibility and fair comparisons - authors .
Approach:	They propose a benchmark that employs a fixed corpus for controlled retrieval for deep search agents.
Outcome:	The new benchmark shows that agents that combine large language models with retrieval tools excel at complex, reasoning-intensive queries.

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Cheng Jiayang, Qianqian Zhuang, Haoran Li, Chunkit Chan, Xin Liu, Lin Qiu, Yangqiu Song

Information Retrieval

Challenge:	Existing grounding approaches work well for simple queries, but many real-world information needs require synthesizing multiple pieces of evidence.
Approach:	They introduce "integrative grounding" to evaluate the ability to ground large language models in external knowledge sources.
Outcome:	The proposed approach is robust to redundant evidence, but rationalizes using internal knowledge when information is incomplete.

Mathematical Proof as a Litmus Test: Revealing Failure Modes of Advanced Large Reasoning Models (2026.acl-long)

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Dadi Guo, Jiayu Liu, Zhiyuan Fan, Zhitao He, Haoran Li, Yuxin Li, Yumeng Wang, Yi R. Fung

Reasoning

Challenge:	Large reasoning models have demonstrated remarkable mathematical problem-solving abilities, but their true reasoning shortcomings are often hidden.
Approach:	They propose to leverage the rigor and methodological complexity of mathematical proofs as a diagnostic tool to expose hidden failures.
Outcome:	The proposed model evaluation exploits the rigor and complexity of proof problems to uncover 10 fine-grained errors.

RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)

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Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, Junran Peng

A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)

Challenge:	Large Language Models (LLMs) have paved the way for complex tasks such as role-playing.
Approach:	They propose a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models.
Outcome:	The proposed framework improves role-playing abilities with 168,093 samples.

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Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu

Challenge:	Empirical natural language processing (NLP) systems involve interoperation among multiple components . a wealth of NLP toolkits exist ( 4), such as spaCy, DKPro, CoreNLP.
Approach:	They propose a unified open-source framework that supports fast development of NLP workflows . framework includes processors for NLP tasks, visualization, and annotation .
Outcome:	The framework offers processors for NLP tasks, visualization, and annotation, and is extensible . it is delivered through two modularized yet integratable open-source projects, Forte and Stave .

Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs (2026.acl-long)

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Haoyue Liu, Zhichao Wang, Yongxin Guo, Haoran Shou, Xiaoying Tang

MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)

Challenge:	Large language models (LLMs) exhibit strong capabilities in reasoning, coding, and complex generation, yet their performance is highly sensitive to prompt design.
Approach:	They propose an API-only framework that decomposes a single prompt into semantic factors and updates selected factors while freezing the rest.
Outcome:	The proposed framework outperforms strong baselines, improves accuracy by up to +4.29 percentage points on average, and reduces optimization cost by 45–87% tokens on MultiArith while reaching peak validation in 1 step.

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Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu

Spelling Error Correction with Soft-Masked BERT (2020.acl-main)

Challenge:	Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents .
Approach:	They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning.
Outcome:	The proposed framework outperforms existing methods in performance and inference efficiency.

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Shaohua Zhang, Haoran Huang, Jicong Liu, Hang Li

Into the Gray Zone: Domain Contexts Can Blur LLM Safety Boundaries (2026.acl-long)

Challenge:	Experimental results show that the proposed method is significantly better than the baselines including the one solely based on BERT.
Approach:	They propose a neural architecture which uses a network for error detection and a system for error correction based on BERT, with the latter connected to the other using what they call soft-masking technique.
Outcome:	The proposed method performs better than baselines including the one solely based on BERT, and is general and may be employed in other language detection-correction problems.

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Ki Sen Hung, Xi Yang, Chang Liu, Haoran Li, Kejiang Chen, Changxuan Fan, Tsun On Kwok, Weiming Zhang, Xiaomeng Li, Yangqiu Song

PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models (2024.acl-long)

Challenge:	a goal of LLM alignment is to balance usefulness with harmlessness, but this conflictes when knowledge serves both legitimate and malicious purposes.
Approach:	They propose a framework that combines safety-research contexts with adversarial interactions to exploit a vulnerability in Jargon queries.
Outcome:	a framework outperforms existing methods in analyzing Jargon queries, a study shows . it achieves 93% of attacks across seven models, while remaining useful, the authors say .

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Haoran Li, Dadi Guo, Donghao Li, Wei Fan, Qi Hu, Xin Liu, Chunkit Chan, Duanyi Yao, Yuan Yao, Yangqiu Song

LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026.findings-acl)

Challenge:	generative large language models (LLMs) exhibit surprising capability and integrate previous tasks into a unified text generation formulation.
Approach:	They propose a privacy evaluation benchmark to quantify the privacy leakage of language models.
Outcome:	The proposed benchmark compares PPLMs with different privacy implementations to find out how privacy leakage is handled.

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Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan, Erik Cambria

OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)

Challenge:	Existing benchmarks for legal general intelligence (GI) are result-oriented and do not evaluate the legal intelligence of large language models (LLMs).
Approach:	They propose a Chinese legal benchmark for evaluating legal GI in large language models . they use recent legal cases and exam questions to create multiple-choice questions .
Outcome:	The proposed benchmarks lack a systematic evaluation of the legal intelligence of large language models (LLMs) the results show that even the best LLMs lagging behind human legal professionals.

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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu