Papers by Yu Hou
From Selection to Refinement: Iterative Optimization for Instruction Data (2026.acl-long)
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Hang Hu, Ziyan Liu, Rujie Wen, Ruihui Hou, Xueyan Wu, Mu Zhang, Jianxing Yu, Tong Ruan, Jingping Liu
| Challenge: | Existing methods to optimize instruction tuning datasets face two main challenges: unreasonable pruning of potentially valuable low-quality data and the persistence of noise or semantic drift during revision. |
| Approach: | They propose an automated iterative framework for instruction data optimization that prunes low-quality data and refines low quality data using feedback-driven iteration. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on seven public benchmark datasets with high data efficiency. |
Structured Confidence–Guided Online Adaptation for LLM-based Multi-Label Classification (2026.findings-acl)
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| Challenge: | Large language models (LLMs) enable zero-shot and few-shot multi-label text classification . but most approaches perform static inference and degrade under streaming test data . |
| Approach: | They propose a structured confidence-guided online adaptation framework for LLM-based multi-label generation without parameter updates. |
| Outcome: | The proposed framework improves Micro-F1 and Macro-F1, with the largest gains on long-tail labels. |
Distantly Supervised Course Concept Extraction in MOOCs with Academic Discipline (2023.acl-long)
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| Challenge: | Existing methods to extract knowledge concepts from MOOCs are noisy and incomplete because of the limited dictionary and diverse MOOC. |
| Approach: | They propose to automatically extract course concepts using distant supervision to eliminate the heavy work of human annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods with 7% absolute improvement in F1 score. |
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences (2021.findings-acl)
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| Challenge: | Recent advances in pretrained language models have shown promising results on commonsense reasoning benchmark datasets. |
| Approach: | They propose a commonsense reasoning benchmark dataset with 4k sentence pairs . they propose 'gamified' model-in-the-loop setup to incentivize challenging samples . |
| Outcome: | The proposed benchmarks show that the proposed model achieves 71% standard accuracy and 51% pairwise accuracy, well below human performance. |
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models (2024.acl-long)
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| Challenge: | Recent studies have developed watermarking algorithms which restrict the generation process to leave an invisible trace for watermark detection. |
| Approach: | They propose a benchmarking procedure that compares different methods to ensure consistent watermarking strength and jointly evaluates their generation and detection performance. |
| Outcome: | The proposed benchmark compares 4 open-source watermarks on 2 LLMs under 2 watermarking strengths and observes the common struggles for current methods on maintaining the generation quality. |
Self-Powered LLM Modality Expansion for Large Speech-Text Models (2024.emnlp-main)
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| Challenge: | Large language models exhibit remarkable performance across diverse tasks . however, these methods require significant resource demands and tend to overfit specific tasks. |
| Approach: | They propose a self-powered LSM that leverages augmented automatic speech recognition data generated by the model itself for more effective instruction tuning. |
| Outcome: | The proposed model mitigates speech anchor bias and improves the fusion of speech and text modalities in large language models. |
Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing (2025.acl-long)
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Kaishuai Xu, Tiezheng Yu, Wenjun Hou, Yi Cheng, Chak Tou Leong, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li
| Challenge: | Existing studies to improve mathematical ability typically involve applying preference learning to step-wise solution pairs, but they overlook critical subtle errors. |
| Approach: | They propose a preference learning framework that injects predefined subtle errors into pivotal tokens to construct hard pairs for error mitigation. |
| Outcome: | Extensive experiments show that the proposed framework improves on Qwen2-7B-Instruct and MATH with 4.5K training samples. |
Evaluating Generative Language Models in Information Extraction as Subjective Question Correction (2024.lrec-main)
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| Challenge: | Modern large language models (LLMs) perform poorly in elementary tasks like relation extraction and event extraction due to two issues in conventional evaluation methods. |
| Approach: | They propose a method to evaluate large language models by incorporating a human annotation schema. |
| Outcome: | The proposed evaluation method improves matching between model outputs and golden labels. |
Program Transfer for Answering Complex Questions over Knowledge Bases (2022.acl-long)
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| Challenge: | Program induction for complex questions over knowledge bases relies on a large number of parallel question-program pairs for the given KB, but the gold program annotations are usually lacking, making learning difficult. |
| Approach: | They propose an approach to leverage program annotations on rich KBs as external supervision signals to aid program induction for low-resourced KB. |
| Outcome: | The proposed approach outperforms SOTA methods on ComplexWebQuestions and WebQuestionSP. |
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction (2023.emnlp-main)
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| Challenge: | Existing evaluation benchmarks focus on pairwise matching, ignoring robustness . current models exhibit frustrating degradation, with a maximum drop of 23.43 F1 score . |
| Approach: | They propose a benchmark that simulates the evaluation of open information extraction models in the real world . they perform experiments on typical models published in the last decade and a representative large language model . |
| Outcome: | The proposed model is rated robust on a knowledge-invariant clique with different syntactic and expressive forms. |
Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach (2023.findings-emnlp)
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| Challenge: | Existing studies on LLMs evaluation with exams are lacking in cognitive research on their overall knowledge structure. |
| Approach: | They conduct an evaluation using a human test dataset based on Bloom Taxonomy to reveal the knowledge structures of Large Language Models and gain insights of their cognitive capabilities. |
| Outcome: | The proposed model can pass AP, SAT, and Leetcode exams, but lacks the cognitive power to perform on human exams. |
FLiText: A Faster and Lighter Semi-Supervised Text Classification with Convolution Networks (2021.emnlp-main)
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| Challenge: | obtaining large amounts of labeled data is expensive. |
| Approach: | They develop a semi-supervised learning framework called FLiText which improves text classification accuracy. |
| Outcome: | The proposed framework improves accuracy of lightweight models on IMDb, Yelp-5, and Yahoo! Answer . the framework improve accuracy by 6.59%, 3.94%, and 3.22% on the datasets of IMDa, Yep-5 and Yahoo. Answer compared with the fully supervised method on the full dataset . |
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making (2021.acl-long)
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| Challenge: | Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. |
| Approach: | They propose a novel EM framework that consists of Heterogeneous Information Fusion and Key Attribute Tree Induction to decouple feature representation from matching decision. |
| Outcome: | The proposed framework outperforms SOTA EM models on 6 public datasets and 3 industrial datasets. |
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)
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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
| Challenge: | Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. |
| Approach: | They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills. |
| Outcome: | The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. |
ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models (2026.findings-acl)
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| Challenge: | Existing benchmarks assess LLM performance in single-course settings and lack systematic evaluation in multi-course scenarios, where a patient’s condition evolves over time. |
| Approach: | They propose to use large language models to assess their performance in multi-course clinical decision-making scenarios where a patient’s condition evolves over time. |
| Outcome: | The proposed model includes 1,275 Chinese and 5,804 English samples across four stages from admission to discharge. |
Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue (2026.findings-acl)
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Guangya Yu, Hui Luo, Qi Ye, Ruihui Hou, Weiyan Zhang, Mingxi Shang, Xuanwu Li, ChunMing Wang, Tong Ruan
| Challenge: | Recent large reasoning models (LLMs) lack dynamic and diverse thinking capabilities . reusing atomic thoughts provides a practical pathway toward dynamic reasoning . |
| Approach: | They propose a framework that extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. |
| Outcome: | The proposed framework extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. |
KoRC: Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding (2023.findings-acl)
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| Challenge: | Existing benchmarks for deep text understanding have encountered two major limitations . most require human annotation of knowledge, which leads to limited knowledge coverage . |
| Approach: | They propose a benchmark to help readers understand a document with prior knowledge . they use massive knowledge bases to guide annotators and large language models to construct knowledgable questions . |
| Outcome: | The proposed benchmarks have limited knowledge coverage and use choices or spans as answers, which results in narrow answer space. |
ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos (2023.emnlp-main)
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Te-Lin Wu, Zi-Yi Dou, Qingyuan Hu, Yu Hou, Nischal Chandra, Marjorie Freedman, Ralph Weischedel, Nanyun Peng
| Challenge: | despite its importance, there are few datasets that cover multimodal counterfactual reasoning . a dataset focusing on this area is limited because of its limited coverage over synthetic environments . |
| Approach: | They develop a video question answering dataset that provides questions on multimodal reasoning . they ask questions about counterfactual hypotheses over visual events . |
| Outcome: | The proposed dataset shows a significant performance gap between models and humans . it provides questions that span physical, social, and temporal dimensions . |
LV-BERT: Exploiting Layer Variety for BERT (2021.findings-acl)
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| Challenge: | Modern pre-trained language models are mostly built upon stereotyped development sets . LV-BERT model obtained by our method outperforms BERT on various downstream tasks . |
| Approach: | They propose to exploit layer variety from the layer type set and the layer order to improve pre-trained models. |
| Outcome: | The proposed model outperforms BERT and its variants on various downstream tasks. |
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. |
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs (2020.aacl-main)
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| Challenge: | Existing methods for concept expansion in MOOCs are inefficient because of the diversity of MOOC courses and rapid updates. |
| Approach: | They propose an end-to-end hierarchical reinforcement learning (HRL) model for concept expansion in MOOCs that employs a two-level mechanism of seed selection and concept expansion. |
| Outcome: | The proposed model improves on nine real MOOC datasets and maintains competitive performance under different settings. |
CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations (2026.findings-acl)
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| Challenge: | LLM-empowered agent simulations generate rich, adaptive, and often nonlinear interaction patterns. |
| Approach: | They propose an automated Causal discovery framework for LLM agent simulations that converts mechanistic hypotheses into computable factors and learns a compact causal representation centered on an emergent target. |
| Outcome: | Experiments across four emergent settings demonstrate the promise of CAMO. |
Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms (2026.findings-acl)
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| Challenge: | Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately in cross-cultural communication with native speakers (NS). |
| Approach: | They use AI to learn English neologisms and write messages using the learned word to an NS friend. |
| Outcome: | The proposed model shows that AI Explanation yields the largest gains over no support in NS-rated competence, while contextual appropriateness judgments show indifference across support. |
CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation (2025.findings-acl)
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Guangya Yu, Yanhao Li, Zongying Jiang, Yuxiong Jin, Li Dai, Yupian Lin, Ruihui Hou, Weiyan Zhang, Yongqi Fan, Qi Ye, Jingping Liu, Tong Ruan
| Challenge: | Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services. |
| Approach: | They propose a Chinese electronic medical records-based dataset for MQCIC and propose CF-IR method that disentangles clinical fact verification and inferential rule reasoning actions. |
| Outcome: | The proposed method outperforms Chain-of-Thought methods on 20 representative LLMs, covering general and medical models. |
SpecCache: Speculative KV Cache Reuse for Efficient RAG Serving (2026.acl-long)
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Zijian Wen, Tao Zhang, Shuangwu Chen, Shenghao Ye, Yu Guo, Qirui Chen, Jingxian Shuai, Yunpeng Hou, Huasen He, null Jianyang
| Challenge: | Retrieval-Augmented Generation (RAG) improves LLMs but faces high prefill latency during long contexts. |
| Approach: | They propose a method that uses deep-layer hidden-state norms to guide token selection . they propose to use deep-layered hidden-status norms as a proxy to guide the token selection. |
| Outcome: | The proposed SpecCache outperforms state-of-the-art (SOTA) benchmarks. |
SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)
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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li
| Challenge: | Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy. |
| Approach: | They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration. |
| Outcome: | The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent . |
MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs (2020.acl-main)
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Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang
| Challenge: | Massive open online courses (MOOCs) are a popular educational platform for advanced research. |
| Approach: | They propose to use MOOCCube to build a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. |
| Outcome: | The proposed datasets show that they can facilitate research in MOOCs. |
On Measures of Biases and Harms in NLP (2022.findings-aacl)
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Sunipa Dev, Emily Sheng, Jieyu Zhao, Aubrie Amstutz, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Akihiro Nishi, Nanyun Peng, Kai-Wei Chang
| Challenge: | Recent studies show that natural language processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality. |
| Approach: | They propose a framework for harms and questions to help practitioners understand biases . they propose measurable measures to detect and mitigate biased groups . |
| Outcome: | The proposed framework provides a framework for harms and questions for practitioners to answer to guide the development of bias measures. |
Toward Automated Robustness Evaluation of Mathematical Reasoning (2026.findings-acl)
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Yutao Hou, Zeguan Xiao, Fei Yu, Yihan Jiang, Ma Shuguang, Zhaoqian Dai, Hailiang Huang, Yun Chen, Guanhua Chen
| Challenge: | Existing robustness evaluations rely on hand-crafted templates or a limited set of perturbation rules, resulting in model failure. |
| Approach: | They propose a framework inspired by software stress testing that generates adversarial variants via a multi-round rewrite-verify loop, ensuring semantic consistency while successfully inducing model failure. |
| Outcome: | The proposed framework generates adversarial variants dynamically for each LLM, minimizing the risk of data contamination. |
Language Models Predict Empathy Gaps Between Social In-groups and Out-groups (2025.naacl-long)
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| Challenge: | Studies of human psychology have shown that people are more motivated to extend empathy to in-group members than out-group member. |
| Approach: | They propose to use language models to study intergroup empathy gap . they use a short description of an experience to predict emotion intensity . |
| Outcome: | The proposed model exhibited strongest intergroup bias among those tested. |
Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning (2024.findings-emnlp)
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Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Dubey, Alexandre Rame, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Leonard Hussenot, Olivier Bachem, Edouard Leurent
| Challenge: | Existing approaches for multi-objective Reinforcement Learning (RL) are difficult due to plurality of preferences and applications. |
| Approach: | They propose a framework for finetuning language models on multiple objectives using conditional language policy. |
| Outcome: | The proposed framework outperforms and Pareto-dominates existing approaches for multi-objective Reinforcement Learning (RL) it does not require training or maintaining multiple models to achieve different trade-offs between the objectives. |
Promoting Pre-trained LM with Linguistic Features on Automatic Readability Assessment (2022.aacl-short)
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| Challenge: | Automatic readability assessment (ARA) aims at classifying the readability level of a text automatically. |
| Approach: | They propose to integrate linguistic features with pre-trained language models to improve the accuracy of ARA. |
| Outcome: | The proposed algorithm improves on the long passage characteristic of ARA using commonly used linguistic features and abundant datasets. |
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision (2024.findings-emnlp)
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| Challenge: | a method for process supervision has shown significant improvements in multi-step problem solving . despite the advances in process supervision, there are still easily observable mistakes in state-of-the-art LLMs. |
| Approach: | They propose a method for automating data curation by using a trained verifier to evaluate intermediate steps generated by a reasoner. |
| Outcome: | The proposed method improves the performance of PaLM 2 on math and coding tasks. |
Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs (2026.acl-long)
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| Challenge: | Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference . cross-module coreference is a crucial missing piece for advancing robust omni-modal reasoning. |
| Approach: | They propose a cross-modal coreference problem to evaluate and enhance Omni-LLMs' reasoning capabilities. |
| Outcome: | Experiments on 13 Omni-LLMs show they lack coreference-aware thinking patterns . the CROSSOMNI dataset yields significant performance gains and generalizes well to collaborative reasoning tasks. |
Self-Generated Critiques Boost Reward Modeling for Language Models (2025.naacl-long)
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Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou
| Challenge: | Existing reward models produce scalar scores and struggle to incorporate critiques in a natural language format. |
| Approach: | They propose a framework that predicts critiques and rewards using self-generated critiques without extra supervision. |
| Outcome: | The proposed framework improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges. |
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization (2022.acl-long)
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| Challenge: | Existing methods to generate educational questions of fairytales or storybooks are difficult to implement due to adults lacking the skills or time to integrate such interactive opportunities. |
| Approach: | They propose a question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. |
| Outcome: | The proposed method performs well on automatic and human evaluation metrics on a newly proposed educational question-answering dataset FairytaleQA. |
Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue (2022.aacl-main)
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| Challenge: | Persuasive dialogue systems are designed for chatbots to communicate with and influence users with specific goals. |
| Approach: | They propose a modular dialogue system framework that integrates factual information and social content into persuasive dialogues. |
| Outcome: | The proposed framework is generalizable to any dialogue tasks that have mixed social and task contents. |
Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search (2026.acl-long)
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Yu Guo, Shenghao Ye, Shuangwu Chen, Zijian Wen, Tao Zhang, Bai Qirui, Dong Jin, Yunpeng Hou, Huasen He, null Jianyang, Xiaobin Tan
| Challenge: | Existing pruning methods rely on sequential revisions and unreliable critique signals . Existing methods fail to detect the loss of answer-critical data . |
| Approach: | They propose a table pruning framework which transforms table pruning to gold trajectory-supervised parallel search. |
| Outcome: | The proposed framework outperforms the strongest baseline pruning framework by 3.2% on various tabular reasoning tasks. |
Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning (2026.findings-acl)
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| Challenge: | Experimental results show that EasyRL consistently outperforms state-of-the-art baselines due to the substantial annotation cost and issues such as model collapse or reward hacking. |
| Approach: | They propose a supervised RL approach with a divide-and-conquer strategy that simulates the human cognitive acquisition curve using easy labeled data. |
| Outcome: | The proposed approach outperforms state-of-the-art models on mathematical and scientific benchmarks using only 10% of easy labeled data. |
Efficient Inference for Large Language Models –Algorithm, Model, and System (2025.emnlp-tutorials)
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| Challenge: | Inference of LLMs incurs high computational costs, memory access overhead, and memory usage, leading to inefficiencies in terms of latency, throughput, power consumption, and storage. |
| Approach: | This tutorial introduces the basics of efficient inference for LLMs and explains how to diagnose efficiency bottlenecks for a given workload on specific hardware. |
| Outcome: | The tutorial introduces the basic concepts of modern LLMs, software and hardware. |
Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting (2020.emnlp-main)
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| Challenge: | Existing methods to fine-tune deep pretrained language models face catastrophic forgetting problems. |
| Approach: | They propose a recall and learn mechanism which integrates pretraining and downstream tasks into a single mechanism. |
| Outcome: | The proposed method achieves state-of-the-art performance on the GLUE benchmark and better average performance than directly fine-tuning of BERT-large. |
Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)
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Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li
| Challenge: | Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored. |
| Approach: | They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching. |
| Outcome: | The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution (2026.acl-long)
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Xiangxi Zheng, Kuang He, Jiayi Hu, Ping Yu, Rui Yan, Yuan Yao, Peng Hou, Anxiang Zeng, Alex Jinpeng Wang
| Challenge: | Existing approaches to chart-to-code generation are constrained by data-centric limitations . authors present a new framework that redesigns both training and alignment data . |
| Approach: | They propose a data-centric framework that redesigns both training and alignment data for chart-to-code generation. |
| Outcome: | The proposed framework outperforms open-source baselines and is competitive with GPT-5. |
Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework (2025.findings-acl)
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Kaishuai Xu, Tiezheng Yu, Yi Cheng, Wenjun Hou, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li
| Challenge: | Existing methods for fine-tuning open-source LLMs are limited to text-based analysis under predefined general criteria. |
| Approach: | They propose a framework that fine-tunes LLMs to replicate the evaluation explanations and judgments of proprietary models. |
| Outcome: | The proposed evaluation framework outperforms existing fine-tuned evaluation methods in effectiveness and robustness. |
Text-to-ES Bench: A Comprehensive Benchmark for Converting Natural Language to Elasticsearch Query (2025.acl-long)
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DonggeXue DonggeXue, Zhili Pu, Zhentao Xia, Hongli Sun, Ruihui Hou, Guangya Yu, Yupian Lin, Yongqi Fan, Jingping Liu, Tong Ruan
| Challenge: | Recent research on text-to-Query has explored using large language models to convert user query intent to executable code. |
| Approach: | They propose a novel semantic parsing task that leverages large language models to generate domain-specific language and post-processing code to support multi-index Elasticsearch queries. |
| Outcome: | The proposed model outperforms DeepSeek-R1 on the large Elasticsearch Dataset (LED) and BirdES datasets. |
UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor (2022.coling-1)
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| Challenge: | Multi-Document Summarization (MDS) uses the extract-then-abstract paradigm, which extracts a relatively short meta-document and then feeds it into the deep neural networks to generate an abstract. |
| Approach: | They propose to use pre-trained language models to calculate document and keyword’s perplexity to boost other metrics for evaluating a document’s salience. |
| Outcome: | The proposed method can be applied as a plug-in to boost other metrics for evaluating a document’s salience, thus improving the subsequent abstract generation. |
AI, Take the Wheel: What Drives Delegation and Trust in Human–Computer Cooperative Question Answering? (2026.findings-acl)
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Maharshi Gor, Yoo Yeon Sung, Yu Hou, Eve Fleisig, Zhu Irene Ying, Tianyi Zhou, Jordan Lee Boyd-Graber
| Challenge: | Human-AI collaboration is already happening, both in proactive delegation and deliberative adoption settings. |
| Approach: | They study delegating a task to AI without seeing its output and evaluating AI suggestions to decide whether to adopt them how AI output shapes final decisions. |
| Outcome: | The proposed game pairs 23 experts with 16 AI agents, capturing 387 delegation and 1440 adoption decisions. |
Large Language Models Can Self-Improve (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have excellent performance in various tasks, but fine-tuning requires extensive supervision. |
| Approach: | They propose to use a pre-trained Large Language Model to generate rationale-augmented answers for unlabeled questions and fine-tune the LLM using those self-generated solutions as target outputs. |
| Outcome: | The proposed approach improves the general reasoning ability of a 540B-parameter LLM without any ground truth label. |
GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration (2025.acl-long)
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| Challenge: | Language models are often miscalibrated, leading to confidently incorrect answers. |
| Approach: | They propose a benchmark for language model calibration that incorporates comparison with human calibration. |
| Outcome: | The proposed metric analyzes model calibration errors and identifies types of miscalibration that differ from human behavior. |
From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent (2026.acl-long)
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Yucheng Wang, Shen Yang, Jifan Yu, Haoxuan Li, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu
| Challenge: | Large language models produce content lacking pedagogical depth when asked to generate lessons . |
| Approach: | They propose a framework that allows teachers to select content according to pedagogical intent and sequence topics so foundations precede applications. |
| Outcome: | The framework achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines. |
CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)
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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions. |
| Approach: | They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges. |
| Outcome: | Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4. |
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)
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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li
| Challenge: | Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) . |
| Approach: | They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries. |
| Outcome: | The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. |
Faster and Better LLMs via Latency-Aware Test-Time Scaling (2025.findings-emnlp)
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| Challenge: | Existing research has overlooked the efficiency of TTS from a latency-sensitive perspective. |
| Approach: | They propose two approaches to achieve latency-optimal TTS by branch-wise parallelism and sequence-wise parallelism. |
| Outcome: | The proposed approach achieves latency-optimal TTS for large models . branch-wise parallelism and sequence-wise parallelism are key approaches . |
Mitigating Label Biases for In-context Learning (2023.acl-long)
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| Challenge: | Existing methods to categorize label biases in in-context learning (ICL) have not addressed all three types of label bias. |
| Approach: | They propose a method that estimates a language model’s label bias using random in-domain words from the task corpus to categorize and detect label biases in ICL. |
| Outcome: | The proposed method significantly improves the performance of GPT-J and GPT-3 on a wide range of tasks. |
HoneyComb: A Flexible LLM-Based Agent System for Materials Science (2024.findings-emnlp)
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| Challenge: | specialized large language models (LLMs) have shown promise in materials science but often struggle with the distinct complexities of materials science tasks. |
| Approach: | They propose a new LLM-based agent system specifically designed for materials science that leverages a reliable materials science knowledge base and a sophisticated tool hub. |
| Outcome: | The proposed system outperforms baseline models across tasks in materials science while ensuring accuracy and relevance. |
Course Concept Expansion in MOOCs with External Knowledge and Interactive Game (P19-1)
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| Challenge: | Existing methods to expand course concepts in MOOCs suffer from semantic drifts and lack of knowledge guidance. |
| Approach: | They propose to use a boundary search method to search for new concepts via external knowledge base and then use heterogeneous features to verify the results. |
| Outcome: | The proposed method improves on the datasets from Coursera and XuetangX. |
Contrastive Bootstrapping for Label Refinement (2023.acl-short)
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| Challenge: | Existing methods for fine-grained classification categorize texts into coarse-gritty classes, but they are suboptimal in real-world scenarios. |
| Approach: | They propose a lightweight contrastive clustering-based bootstrapping method to iteratively refine the labels of passages. |
| Outcome: | The proposed method outperforms the state-of-the-art methods by a large margin on NYT and 20News datasets. |
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training (2023.findings-emnlp)
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Jaehyung Kim, Yuning Mao, Rui Hou, Hanchao Yu, Davis Liang, Pascale Fung, Qifan Wang, Fuli Feng, Lifu Huang, Madian Khabsa
| Challenge: | Several perspectives of robustness for pre-trained language models have been studied independently, but lacking a unified consideration in multiple perspectives. |
| Approach: | They propose a technique to enhance the multi-perspective robustness of LMs by introducing adversarial perturbation while the model parameters are selectively updated upon their relative importance. |
| Outcome: | The proposed technique improves the robustness of LMs by incorporating four perspectives on model robustness. |
VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models (2025.coling-main)
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| Challenge: | Visual Language Models (VLMs) have been gaining popularity with large language models, but few attempts have been made to incorporate efficient linear Recurrent Neural Networks (RNNs) into VLMs. |
| Approach: | They propose a linear RNN model with a data-dependent recurrence and sandwich prompts to enhance modeling capabilities and a 2D image scanning mechanism to enrich the processing of visual sequences. |
| Outcome: | The proposed model achieves competitive performance compared to Transformer-based models on various benchmarks. |
Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models (2023.emnlp-main)
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Yifan Hou, Jiaoda Li, Yu Fei, Alessandro Stolfo, Wangchunshu Zhou, Guangtao Zeng, Antoine Bosselut, Mrinmaya Sachan
| Challenge: | Recent work has shown that language models (LMs) have strong multi-step (i.e., procedural) reasoning capabilities. |
| Approach: | They propose a mechanistic interpretation of language models for multi-step reasoning tasks by introducing a new probing approach that recovers the reasoning tree from the model’s attention patterns. |
| Outcome: | The proposed model implicitly embeds a reasoning tree resembling the correct reasoning process within it, and detects the information from the model’s attention patterns for most examples. |
Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models (2024.lrec-main)
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| Challenge: | Existing studies have explained to what extent LLMs extract conflicting knowledge from the provided text, but they neglect the necessity to reason with conflicting information. |
| Approach: | They construct a dataset for knowledge conflict resolution examination in the form of question answering that divides reasoning with conflicting knowledge into three levels. |
| Outcome: | The proposed dataset enables analysis of reasoning with conflicting knowledge in the form of question answering. |
Learn to Not Link: Exploring NIL Prediction in Entity Linking (2023.findings-acl)
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| Challenge: | Entity linking models have been successful in capturing semantic features, but the NIL prediction problem has not been addressed. |
| Approach: | They propose an entity linking dataset that categorizes mentions linking to NIL into Missing Entity and Non-Entity Phrases. |
| Outcome: | The proposed dataset categorizes mentions linking to NIL into Missing Entity and Non-Entity Phrase categories and ensures the presence of mentions by human annotation and entity masking. |
A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations, but they are plagued by the Knowledge Hallucination problem. |
| Approach: | They propose a method that exploits the dialogue-knowledge interaction to reduce hallucination by using external knowledge resources to generate more informative responses. |
| Outcome: | The proposed method reduces hallucination without disrupting other dialogue performance while keeping adaptive to different generation models. |
When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables (2026.acl-long)
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Shenghao Ye, Yu Guo, Dong Jin, Yuxiang Wang, Yikai Shen, Yunpeng Hou, Shuangwu Chen, null Jianyang, Xiaofeng Jiang
| Challenge: | Extensive research shows that noisy data significantly degrades the performance of table reasoning in real-world applications. |
| Approach: | They propose a dual denoising framework for complex questions and large-scale tables that uses Tree-guided table pruning to remove irrelevant data step by step. |
| Outcome: | The proposed framework achieves outstanding performance on TableQA tasks with complex questions and large-scale tables. |