Papers by Hao Fei
Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling (2023.acl-long)
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| Challenge: | Existing research on multimodal relation extraction (MRE) faces internal-information over-utilization and external-information under-exploitation. |
| Approach: | They propose a framework that implements internal-information screening and external-information exploiting to address these challenges. |
| Outcome: | The proposed framework outperforms the current best model on the benchmark dataset. |
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection (2023.findings-emnlp)
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| Challenge: | Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning. |
| Approach: | They propose to apply world knowledge to enhance OOD detection performance through selective generation from large language models (LLMs) they propose to extract visual objects from each image to fully capitalize on the aforementioned world knowledge. |
| Outcome: | The proposed method outperforms the state-of-the-art on visual OOD detection on in-distribution (ID) samples. |
EmpathyEar: An Open-source Avatar Multimodal Empathetic Chatbot (2024.acl-demos)
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| Challenge: | EmpathyEar is an open-source, avatar-based multimodal empathetic chatbot . currently, ERG systems rely on text, sound, and vision . |
| Approach: | They propose an open-source, avatar-based multimodal empathetic chatbot to fill the gap in traditional text-only ERG systems. |
| Outcome: | The proposed system enables users to generate emotional responses to user queries . it can also generate avatars with talking faces and synchronized speeches . |
MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter (2023.emnlp-main)
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| Challenge: | Language Models (LMs) have demonstrated impressive molecule understanding ability on 1D text-related tasks, but lack 2D graph perception, a critical ability of human professionals in comprehending molecules’ topological structures. |
| Approach: | They propose to combine a cross-modal projector and a uni-modal adapter to enable an LM to understand both text- and graph-based molecular contents via a Q-Former. |
| Outcome: | The proposed model outperforms the baselines on tasks such as molecule captioning, IUPAC name prediction, and molecule-text retrieval. |
Synergizing Large Language Models and Pre-Trained Smaller Models for Conversational Intent Discovery (2024.findings-acl)
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| Challenge: | Current attempts at CID rely on pretrained Small Language Models (SLMs) this lacks the ability to label new intents and is a challenge for small language models. |
| Approach: | They propose to combine Large Language Models (LLMs) with pre-trained SLMs for CID to enhance the semantic comprehension of LLMs. |
| Outcome: | The proposed approach improves the semantic comprehension of LLMs and the operational agility of SLMs by realigning existing descriptors within the SLM’s feature space to correct cluster distortion and promote robust learning of representations. |
When Words Smile: Generating Diverse Emotional Facial Expressions from Text (2025.emnlp-main)
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| Challenge: | Existing systems that generate only coarse facial expressions ignore the rich and dynamic nature of face-to-face communication. |
| Approach: | They propose an end-to-end text-to expression model that explicitly focuses on emotional dynamics. |
| Outcome: | The proposed model outperforms baselines on 15,000 text–3D expression pairs on a large-scale dataset. |
Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents (2026.findings-acl)
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Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, Zhixu Li
| Challenge: | Existing LLMs struggle to identify errors in financial documents, a study shows . 18% of financial practitioners make errors daily, one-third make errors several times weekly, and 59% make errors multiple times monthly. |
| Approach: | They introduce FinED-Bench, a publicly available Benchmark for financial error detection . it covers nine real-world financial scenarios and includes over 900 documents in 2025 . supervised fine-tuning can significantly improve the performance of weaker LLMs, they show . |
| Outcome: | The proposed benchmark covers nine real-world financial scenarios and includes over 900 documents reported in 2025 that are unseen by existing language models. |
Guided Knowledge Generation with Language Models for Commonsense Reasoning (2024.findings-emnlp)
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| 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. |
Conversation Disentanglement with Bi-Level Contrastive Learning (2022.findings-emnlp)
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| Challenge: | Existing methods focus on pairwise utterance relations but pay inadequate attention to utterant-to-context relation modeling. |
| Approach: | They propose a general disentangle model based on bi-level contrastive learning that brings closer utterances in the same session while encouraging each utterrance to be near its clustered session prototypes in representation space. |
| Outcome: | The proposed model achieves state-of-the-art performance on both settings across public datasets. |
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts (2025.acl-long)
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Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang, Haoyuan Li, Zhelun Yu, Fangxun Shu, Weilong Dai, Hao Jiang, Fei Wu, Leilei Gan
| Challenge: | Existing studies on text-to-image (T2I) models focus on text alignment, image quality, and object composition capabilities. |
| Approach: | They propose a T2I-FactualBench benchmark to evaluate the factuality of knowledge-intensive concept generation. |
| Outcome: | The proposed framework evaluates the factuality of knowledge-intensive concept generation tasks. |
A Survey of Ontology Expansion for Conversational Understanding (2024.emnlp-main)
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| Challenge: | Current methods for conversational understanding rely on static ontologies, limiting their ability to handle new and unforeseen user needs. |
| Approach: | They propose to review the state-of-the-art techniques in OnExp for conversational understanding and highlight emerging frontiers . they categorize existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. |
| Outcome: | The proposed methods highlight several emerging frontiers in OnExp to improve agent performance in real-world scenarios and discuss their corresponding challenges. |
Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge (2025.acl-long)
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| Challenge: | Existing methods for hyperbole and metaphor detection focus on superficial text features, ignoring the associations of hyperbola and metaphor . Existing frameworks focus on identifying superficial text, focusing on superficial features . |
| Approach: | They propose an emotion-guided hyperbole and metaphor detection framework based on bidirectional dynamic interaction. |
| Outcome: | The proposed framework outperforms baseline methods on four datasets. |
MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction (2021.findings-acl)
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| Challenge: | Existing studies on document-level relation extraction focus on sentencelevel RE, but recent studies reveal that a large number of relations can actually be expressed through multiple sentences, which necessitates document- level RE. |
| Approach: | They propose a document-level relation extraction model that captures local and global contextual information as well as close and distant mention interactions. |
| Outcome: | The proposed model outperforms state-of-the-art models on three widely used datasets, namely DocRED, CDR, and GDA. |
CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025.findings-emnlp)
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| Challenge: | Existing work focuses on enabling LLMs to leverage legal rules to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. |
| Approach: | They propose a legal paragraph prediction task that aims to predict the legal paragraph given criminal facts and a framework CLEAR to enhance their legal reasoning ability. |
| Outcome: | The proposed model improves the ability of LLMs to analyze legal cases with the guidance of legal rule insights. |
ProtT3: Protein-to-Text Generation for Text-based Protein Understanding (2024.acl-long)
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| Challenge: | Language Models excel in understanding textual descriptions of proteins, but struggle to process texts. |
| Approach: | They propose a framework for Protein-to-Text Generation for Text-based Protein Understanding that integrates a PLM as its protein understanding module. |
| Outcome: | The proposed framework surpasses existing baselines and is highly efficient in protein-to-text generation. |
PairCoder: Pair Programming-Inspired Two-Agent Collaboration for Code Generation (2026.findings-acl)
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| Challenge: | Existing multi agent frameworks for large language models are brittle on code generation tasks. |
| Approach: | They propose a framework that brings pair programming to autonomous LLM collaboration. |
| Outcome: | Using PairCoder, large language models achieve better results on code generation tasks and reduce token usage by 40% to 70% on eight representative backbones. |
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions (2020.coling-main)
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| Challenge: | a novel context-aware dynamic convolution network is proposed to better leverage the local contexts when dynamically generating convolution kernels. |
| Approach: | They propose a dynamic convolution network to leverage local contexts when generating convolution kernels. |
| Outcome: | The proposed frameworks achieve state-of-the-art on two benchmark datasets. |
DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling (2025.emnlp-main)
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Hao Sun, Zile Qiao, Bo Wang, Guoxin Chen, Yingyan Hou, Yong Jiang, Pengjun Xie, Fei Huang, Yan Zhang
| Challenge: | Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs). |
| Approach: | They propose a framework that decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding. |
| Outcome: | The proposed framework decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding. |
Fine-Tuning Language Models with Reward Learning on Policy (2024.naacl-long)
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| Challenge: | Reinforcement learning from human feedback (RLHF) is an effective approach to align large language models (LLMs) to human preferences. |
| Approach: | They propose a framework that refines a reward model using policy samples to keep it on-distribution. |
| Outcome: | The proposed framework outperforms the state-of-the-art on three benchmark datasets showing that it can learn robust representations of policy samples. |
RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)
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Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun
| Challenge: | Existing benchmarks focus on character-centric approach and fail to reflect real-world applications. |
| Approach: | RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds. |
| Outcome: | RMTBench features 80 diverse characters and over 8,000 dialogue rounds. |
From Multimodal LLM to Human-level AI: Modality, Instruction, Reasoning, Efficiency and beyond (2024.lrec-tutorials)
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| Challenge: | This tutorial aims to deliver a comprehensive review of cutting-edge research in MLLMs. |
| Approach: | This tutorial will review cutting-edge research in MLLMs and examine the impact of ML in learning and reasoning. |
| Outcome: | This course will review cutting-edge research in MLLMs and examine the impact of ML models on learning, learning, and multimodal reasoning. |
Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination (2023.acl-long)
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| Challenge: | Current neural machine translation (NMT) relies on parallel sentences, which obstructs the development of NMT for minor languages. |
| Approach: | They propose an unsupervised multimodal machine translation setup where the model is trained with source-text image pairs and tested with only source- text inputs. |
| Outcome: | The proposed model outperforms the baseline model on the task and setup, helping yield translations with better completeness, relevance and fluency without relying on paired images. |
Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation (2022.findings-emnlp)
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| Challenge: | Existing methods to detect toxic generation of pretrained language models rely on templates, data extraction, crowdsourcing workers or automatic generation. |
| Approach: | They propose a method to construct adversarial contexts conditioned on a given response . they augment existing dataset BAD+ and construct a new dataset B AD+ . |
| Outcome: | The proposed method can detect toxic or biased content in large pretrained language models. |
DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering (2025.acl-long)
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Rong Cheng, Jinyi Liu, Yan Zheng, Fei Ni, Jiazhen Du, Hangyu Mao, Fuzheng Zhang, Bo Wang, Jianye Hao
| Challenge: | Existing approaches to multi-hop question answering struggle to identify and organize dynamic knowledge . et al., 2023; Liu e.t. al. 2023) suggest a dual-process framework for multi-step reasoning . |
| Approach: | They propose a synergistic dual-process framework that integrates reasoning and retrieval. |
| Outcome: | The proposed framework improves answer accuracy and coherence even in smaller-scale models. |
Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts (2024.lrec-main)
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| Challenge: | Existing methods for OOD intent detection are limited to single dialogue turns. |
| Approach: | They propose a context-aware OOD intent detection framework to model multi-turn contexts in OOD context detection tasks using unlabeled data. |
| Outcome: | The proposed framework improves the F1-OOD score by 29% on multi-turn OOD detection tasks compared to the previous best method. |
XNLP: An Interactive Demonstration System for Universal Structured NLP (2024.acl-demos)
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| Challenge: | Structured Natural Language Processing (XNLP) is an important subset of NLP that entails understanding the underlying semantic or syntactic structure of texts. |
| Approach: | They propose a XNLP demonstration system that leverages LLM to achieve universal XnLP with one model for all with high generalizability. |
| Outcome: | The proposed system advances in multiple aspects, including universal XNLP modeling, high performance, interpretability, scalability, and interactivity. |
WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild? (2025.emnlp-main)
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An-Lan Wang, Jingqun Tang, Lei Liao, Hao Feng, Qi Liu, Xiang Fei, Jinghui Lu, Han Wang, Hao Liu, Yuliang Liu, Xiang Bai, Can Huang
| Challenge: | Existing benchmarks for document understanding in the wild are based on scanned or digital documents . however, these benchmarks fail to capture the challenges posed by documents in the real world . |
| Approach: | They propose a new benchmark that incorporates a diverse set of manually captured document images reflecting real-world conditions. |
| Outcome: | The proposed model is based on a set of manually captured document images reflecting real-world conditions and is compared with digital or scanned documents. |
Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction (2023.findings-acl)
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| Challenge: | Recent efforts on cross-lingual relation extraction (XRE) leverage language-consistent structural features from the universal dependency resource. |
| Approach: | They propose to construct a type of code-mixed UD forest that combines UD and source-/target-side UD structures to achieve unbiased transfer. |
| Outcome: | The proposed UD forest achieves significant performance gains on ACE XRE benchmark datasets. |
Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework (2025.acl-long)
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Jundong Xu, Hao Fei, Meng Luo, Qian Liu, Liangming Pan, William Yang Wang, Preslav Nakov, Mong-Li Lee, Wynne Hsu
| Challenge: | Existing systems fail to fully leverage the structure of logical tasks throughout the reasoning process, causing bottlenecks in efficiency and efficacy. |
| Approach: | They propose a logic-complete reasoning framework, Aristotle, which integrates symbolic expressions and logical rules into the entire reasoning process. |
| Outcome: | The proposed framework outperforms state-of-the-art reasoning frameworks in accuracy and efficiency. |
Visual Prompt Tuning for Few-Shot Text Classification (2022.coling-1)
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| Challenge: | Existing work on pretraining models for text classification uses image encoders instead of visual prompts. |
| Approach: | They propose a method to deploy large-scale pre-trained models in the prompt-tuning paradigm in few-shot learning. |
| Outcome: | The proposed method outperforms the most recent prompt-tuning methods on five public text classification datasets. |
Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP (2020.findings-emnlp)
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| Challenge: | Existing work on integrating syntactic information into neural networks uses a single tree, such as a constituency or a dependency tree. |
| Approach: | They propose a method to integrate heterogeneous structure knowledge into a unified sequential LSTM encoder. |
| Outcome: | The proposed method outperforms tree encoders on four syntax-dependent tasks and is efficient and accurate. |
Efficient Cross-modal Prompt Learning with Semantic Enhancement for Domain-robust Fake News Detection (2025.coling-main)
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| Challenge: | Existing MFND methods conduct cross-modal information interaction at later stage, resulting in weak generalization ability. |
| Approach: | They propose an automatic multi-modal fake news detection method that exploits cross-modal information interaction at later stage. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three MFND benchmarks. |
Teaching Small Language Models Reasoning through Counterfactual Distillation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance in a wide range of downstream tasks. |
| Approach: | They propose a counterfactual distillation framework that leverages LLMs to generate high-quality counterfacts and utilizes multi-view CoT to enhance the diversity of reasoning samples. |
| Outcome: | The proposed framework enhances reasoning capabilities of large language models and is more robust to OOD data. |
Retrofitting Structure-aware Transformer Language Model for End Tasks (2020.emnlp-main)
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| Challenge: | Experimental results show that structure-aware Transformer language model achieves improved perplexity, meanwhile inducing accurate syntactic phrases. |
| Approach: | They propose to exploit syntactic distance to encode phrasal constituency and dependency connection into Transformer language model and leverage it for structure integration. |
| Outcome: | The proposed model achieves significant improvements for both semantic- and syntactic-dependent tasks. |
Reasoning Implicit Sentiment with Chain-of-Thought Prompting (2023.acl-short)
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| Challenge: | In implicit sentiment analysis, the opinion cues come in an implicit and obscure manner. |
| Approach: | They propose a three-step prompting principle for THOR to step-by-step induce the implicit aspect, opinion and finally the sentiment polarity. |
| Outcome: | The proposed framework pushes the state-of-the-art (SoTA) by over 6% F1 on supervised setup and more strikingly, boosts the SoTA by over 50% F1 with THOR+GPT3. |
Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling (2021.findings-acl)
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| Challenge: | Existing studies use only one singleton syntax for semantic role labeling (SRL). |
| Approach: | They propose a TreeLSTM-based integration that integrates phrasal boundaries and semantic relations from dependency into a labelaware GCN solution for simultaneously modeling syntactic edges and labels. |
| Outcome: | The proposed system achieves state-of-the-art performance on span-based and dependency-based SRL. |
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages (2022.acl-long)
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| Challenge: | Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages. |
| Approach: | They propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages. |
| Outcome: | The proposed framework can learn effective FGET models for low-resource languages even without human-labeled data. |
Domain Incremental Lifelong Learning in an Open World (2023.findings-acl)
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| Challenge: | Existing approaches to lifelong learning (LL) models require access to task identities in the testing phase or cannot handle samples from unseen tasks. |
| Approach: | They propose a dynamic architecture-based lifelong learning model that tries to learn a sequence of tasks with a prompt-enhanced language model. |
| Outcome: | The proposed model outperforms state-of-the-art models in handling unseen tasks and focuses on task-level prompts to capture knowledge from different granularities. |
Improving Text Understanding via Deep Syntax-Semantics Communication (2020.findings-emnlp)
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| Challenge: | Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance. |
| Approach: | They propose a deep neural communication model between syntax and semantics to improve the performance of text understanding. |
| Outcome: | The proposed model outperforms baseline models on syntax-dependent tasks by a large margin. |
Long-Tailed Question Answering in an Open World (2023.acl-long)
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| Challenge: | Existing QA approaches require access to seen tasks or do not explicitly model samples from unseen tasks. |
| Approach: | They propose an open-tailed QA model that encourages knowledge sharing between head, tail and unseen tasks and explicitly mines knowledge from a large pre-trained language model. |
| Outcome: | The proposed model outperforms the state-of-the-art on a large-scale dataset. |
MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding (2026.findings-acl)
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| Challenge: | Existing methods for video retrieval rely on embedding-based full-corpus scanning, but there is a bottleneck in semantic asymmetry and computational redundancy. |
| Approach: | They propose a multi-agent framework that rethinks retrieval as cooperative reasoning . they parse raw videos into a structured semantic library, enabling explicit attribute-level indexing . |
| Outcome: | The proposed framework bridges the granularity mismatch gap by parsing raw videos into a structured semantic library . it employs a Logic-aware Debate mechanism with a strict veto protocol . the proposed framework achieves competitive performance without task-specific fine-tuning . |
Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing (2024.acl-long)
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| Challenge: | Structured Sentiment Analysis (SSA) is a problem of bi-lexical dependency graph parsing due to the internal structures of spans neglected. |
| Approach: | They propose to use latent spans as latent subtrees to model internal structures of spans and leverage TreeCRFs to extract the complete opinion tuple from a sentence. |
| Outcome: | The proposed method performs significantly better than all previous bi-lexical methods, achieving new state-of-the-art. |
Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction (2024.findings-acl)
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| Challenge: | Existing studies on IE tasks have focused on recognizing and analyzing cross-modal information . a multimodal large language model (MLLM) is developed to analyze IE across modalities . |
| Approach: | They propose a multimodal large language model (MLLM) capable of grounding information from all modalities. |
| Outcome: | The proposed framework provides a framework to analyze IE tasks over various modalities and their fine-grained groundings. |
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis (2022.acl-long)
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| Challenge: | Structured sentiment analysis is a dependency parsing problem, with imbalanced label distributions and large text spans. |
| Approach: | They propose a novel labeling strategy which contains two sets of token pair labels . they propose tuple extraction problem with a more balanced label distribution . |
| Outcome: | The proposed model outperforms existing models on 5 benchmark datasets in four languages. |
David vs. Goliath: Cost-Efficient Financial QA via Cascaded Multi-Agent Reasoning (2025.findings-emnlp)
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| Challenge: | Large language models have demonstrated remarkable reasoning capabilities, but performance in FQA remains limited. |
| Approach: | They propose a low-cost yet effective framework that enables small LLMs to perform complex reasoning tasks without expensive models. |
| Outcome: | The proposed framework outperforms the best open-source model on BizBench by 10.46% and achieves competitive performance to GPT-3.5 using significantly fewer parameters. |
Optimizing Instruction Synthesis: Effective Exploration of Evolutionary Space with Tree Search (2024.findings-emnlp)
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| Challenge: | Extensive research has highlighted the quality of instruction data is essential for the success of this alignment. |
| Approach: | They propose a framework for iteratively improving existing instruction data by using Monte Carlo tree search to find suitable prompts that align the language model to effectively learn multiple skills. |
| Outcome: | The proposed framework improves the evaluation scores of seed instruction data, raising the average evaluation scores from 2.19 to 3.81. |
Divide and Conquer: Legal Concept-guided Criminal Court View Generation (2024.findings-emnlp)
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| Challenge: | Existing methods for creating rationales for criminal cases do not pay enough attention to the important legal concepts. |
| Approach: | They propose a legal concept-guided court view generation framework that generates rationales based on predicted legal concepts . they first divide the court view into sub-views, then employ a solver and verifier to generate and select rationale. |
| Outcome: | The proposed model generates coherent and coherent court views on a real-world criminal case dataset. |
OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction (2022.coling-1)
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| Challenge: | Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text. |
| Approach: | They propose a tagging scheme and a model to form EE as word-word relation recognition using parallel grid tapping. |
| Outcome: | The proposed model achieves state-of-the-art on 3 overlapped and nested EE benchmarks and faster than baselines. |
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment (2024.lrec-main)
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| Challenge: | Large language models (LLMs) can reveal toxic or offensive content inadvertently or intentionally. |
| Approach: | They propose to control the diversity of both sides according to the number of samples for fine-tuning, which can directly reflect their impact. |
| Outcome: | The proposed approach improves the performance of large language models after fine-tuning. |
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions (2023.acl-long)
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| Challenge: | A moral dialogue system aligned with users’ values could enhance conversation engagement and user connections. |
| Approach: | They propose a framework to train and evaluate moral dialogue systems based on communication mechanisms of morality and a method to construct moral discussions between simulated users and the dialogue system. |
| Outcome: | The proposed framework can train and evaluate moral dialogue systems based on simulated users and their values . |
Entity-centered Cross-document Relation Extraction (2022.emnlp-main)
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Fengqi Wang, Fei Li, Hao Fei, Jingye Li, Shengqiong Wu, Fangfang Su, Wenxuan Shi, Donghong Ji, Bo Cai
| Challenge: | Existing methods for relation extraction only use text snippets surrounding target entities in multiple documents. |
| Approach: | They propose a relation-extraction model that uses cross-path entity relation attention to detect the semantic relations between entities in a given text. |
| Outcome: | The proposed method outperforms the state-of-the-art methods in the dataset CodRED by 10%. |
Understanding Generalization in Role-Playing Models via Information Theory (2026.findings-acl)
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| Challenge: | Existing methods to characterize RPM generalization are inadequate . existing methods do not provide a fine-grained diagnosis of distribution shifts . |
| Approach: | They propose a reasoning-based effective mutual information difference (R-EMID) to measure RPM performance degradation in an interpretable way. |
| Outcome: | The proposed model predicts the worst-case generalization performance of RPMs and reveals how shifts contribute to degradation. |
High-order Refining for End-to-end Chinese Semantic Role Labeling (2020.aacl-main)
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| Challenge: | Current semantic role labeling methods are limited to short-term features and local decisions. |
| Approach: | They propose a high-order refining mechanism to perform interaction between all predicate-argument pairs via attention calculation. |
| Outcome: | The proposed model achieves state-of-the-art on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, while relieving the long-range dependency issues. |
Retrieved In-Context Principles from Previous Mistakes (2024.emnlp-main)
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| Challenge: | Recent advances in in-context learning (ICL) have limited customization and inadequate error coverage. |
| Approach: | They propose a method to retrieve in-context principles from mistakes to improve model performance. |
| Outcome: | The proposed framework enhances model performance when applied to various prompting strategies. |
CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models (2025.acl-long)
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| Challenge: | Existing studies on hallucinations in large language models are limited to a single scenario, either cross-lingual or cross-modal. |
| Approach: | They propose a joint Cross-lingual and Cross-modal hallucinations benchmark to fill this gap . they incorporate cross-lingual, cross-modal scenarios to assess hallucinic capabilities . |
| Outcome: | The proposed benchmark incorporates both cross-lingual and cross-modal hallucination scenarios to assess the cross-linguistic and crossmodal capabilities of LLMs. |
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus (2020.acl-main)
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| Challenge: | Existing approaches to semantic role labeling (SRL) are focusing on the English language. |
| Approach: | They propose a method for semantic role labeling that uses corpus translation to build training datasets from SRL annotations. |
| Outcome: | The proposed method is highly effective and can improve the target-language performance significantly. |
Actively Learn from LLMs with Uncertainty Propagation for Generalized Category Discovery (2024.naacl-long)
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| Challenge: | Generalized category discovery (GCD) is a crucial task in open-world computing, where new categories frequently emerge, necessitating models that can adapt and learn continually. |
| Approach: | They propose to integrate the feedback from LLMs into an active learning paradigm to simplify the labeling task and minimize the spread of inaccurate feedback. |
| Outcome: | The proposed approach significantly improves baseline models at a nominal average cost. |
Estimating Soft Labels for Out-of-Domain Intent Detection (2022.emnlp-main)
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| Challenge: | Existing methods to detect out-of-dominance (OOD) intents are limited by the lack of OOD samples. |
| Approach: | They propose an adaptive soft pseudo labeling method that can estimate soft labels for pseudo OOD samples when training OOD detectors. |
| Outcome: | The proposed method outperforms competing methods on three benchmark datasets and consistently outperformed previous methods. |
Dynamic Emotion and Personality Profiling for Multimodal Deception Detection (2026.acl-long)
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| Challenge: | Existing methods for deception detection lack sample-level dynamic annotations for emotions and personality. |
| Approach: | They propose a multi-model multi-prompt annotation scheme and a strict label quality evaluation standard for deception, emotion, and personality annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art models on the MDPE and DDEP datasets. |
AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)
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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu
| Challenge: | Significant concerns emerge when addressing cultural sensitivity and local values. |
| Approach: | They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. |
| Outcome: | The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. |
Compilable Neural Code Generation with Compiler Feedback (2022.findings-acl)
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Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, Qun Liu
| Challenge: | Existing deep-learning approaches model code generation as text generation, but few of them account for compilability of the generated programs. |
| Approach: | They propose a three-stage pipeline utilizing compiler feedback for compilable code generation to improve compilability. |
| Outcome: | The proposed pipeline improves compilability of generated programs by combining compiler feedback, language model fine-tuning, and compilable discrimination. |
Advancing Sequential Numerical Prediction in Autoregressive Models (2025.acl-short)
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| Challenge: | Autoregressive models are the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences. |
| Approach: | They propose a novel approach to entropy loss by extending the Earth Mover’s Distance to preserve ordinal relationships between numerical values and sequence-level to penalize the overall discrepancy between predicted and actual sequences. |
| Outcome: | Extensive experiments show that NTIL improves numerical prediction and integrates effectively with LLMs/MLLMs. |
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction (2022.coling-1)
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| Challenge: | Existing methods for ECPE fail to model specific features and interactive features in between, or suffer from inconsistency of label prediction. |
| Approach: | They propose to align ECPE with a feature-task alignment mechanism to model emotion-&cause-specific features and the shared interactive feature. |
| Outcome: | The proposed model outperforms existing systems on all ECA subtasks. |
DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis (2023.findings-acl)
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Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji
| Challenge: | a new task of conversational aspect-based sentiment analysis (DiaASQ) is designed to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. |
| Approach: | They propose a task of conversational aspect-based sentiment quadruple analysis to detect the quadrangle of target-aspect-opinion-sentiment in a dialogue. |
| Outcome: | The proposed task is based on a high-quality dataset in Chinese and English . it improves the end-to-end quadruple prediction and integrates rich feature representations . |
Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have enabled the development of powerful autonomous systems. |
| Approach: | They propose a model trained through dialectical alignment to enforce perspective-invariant reasoning. |
| Outcome: | The proposed model mitigates attribution inconsistency and significantly improves fault resolution rates in ambiguous scenarios. |
Faithful Logical Reasoning via Symbolic Chain-of-Thought (2024.acl-long)
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| Challenge: | SymbCoT is a framework that integrates symbolic expressions and logic rules with CoT prompting. |
| Approach: | They propose a Symbolic Chain-of-Thought framework that integrates symbolic expressions and logic rules with CoT prompting. |
| Outcome: | The proposed framework improves on 5 standard datasets with symbolic expressions and rules . it shows that it is more faithful, flexible, and explainable than the current method . |
COLD: A Benchmark for Chinese Offensive Language Detection (2022.emnlp-main)
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| Challenge: | Offensive language detection is crucial for maintaining a civilized social media platform and deploying pre-trained language models. |
| Approach: | They propose a benchmark benchmark for Chinese offensive language analysis including a Chinese Offensive Language Dataset and a baseline detector which is trained on the dataset. |
| Outcome: | The proposed benchmark contributes to Chinese offensive language detection which is challenging for existing resources. |
InTriage: Intelligent Telephone Triage in Pre-Hospital Emergency Care (2025.emnlp-demos)
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| Challenge: | Existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triages rates. |
| Approach: | They propose to use an AI-driven multilingual TT system to provide decision support for triage. |
| Outcome: | The proposed system achieves word error rate of 14.57% for speech recognition and an F1 score of 73.34% for key information extraction. |
Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment (2023.acl-long)
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| Challenge: | Current captioning models are limited to the English language due to the largescale paired image-caption datasets. |
| Approach: | They propose to integrate the scene graph (SG) structures and the syntactic constituency trees into a captioner to improve captioning relevancy and fluency. |
| Outcome: | The proposed model improves captioning relevancy and fluency on English-Chinese transfers. |
What Factors Influence LLMs’ Judgments? A Case Study on Question Answering (2024.lrec-main)
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Lei Chen, Bobo Li, Li Zheng, Haining Wang, Zixiang Meng, Runfeng Shi, Hao Fei, Jun Zhou, Fei Li, Chong Teng, Donghong Ji
| Challenge: | Existing studies indicate that Large Language Models perform at a level comparable to humans with advantages of speed and cost-effectiveness in different fields. |
| Approach: | They propose to introduce four unexplored factors and a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies. |
| Outcome: | The proposed dimensions of question difficulty and answer quantity provide valuable insights into optimizing LLMs’ performance as judges. |
Generating Visual Spatial Description via Holistic 3D Scene Understanding (2023.acl-long)
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| Challenge: | Existing VSD work focuses on skewed spatial understanding of target objects . Existing work merely models the 2D geometrical vision features . |
| Approach: | They propose to incorporate 3D scene features into visual spatial description tasks by sampling topologically-diverse subgraphs from Go3D-S2G. |
| Outcome: | The proposed framework outperforms baselines on two VSD datasets and produces more spatially-diversified generation. |
Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)
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Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang
| Challenge: | Current document image parsing solutions rely on specialized models or generate content autoregressively. |
| Approach: | They propose a multimodal document image parsing model that integrates specialized models with autogeneous content generation. |
| Outcome: | The proposed model achieves state-of-the-art performance across diverse page-level and element-level settings while ensuring superior efficiency. |