Papers by Xiang Hu
Word Graph Guided Summarization for Radiology Findings (2021.findings-acl)
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| Challenge: | Existing studies focus on introducing salient word information to general text summarization framework to guide selection of key content in radiology findings. |
| Approach: | They propose a method for automatic impression generation using word graphs and a Word Graph guided Summarization model to capture critical words and their relations. |
| Outcome: | The proposed method is validated on two datasets, OPENI and MIMIC-CXR. |
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons (2023.acl-long)
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Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj Ammanabrolu
| Challenge: | Existing dialogue agents, while able to produce human-like responses, often do not model goal-driven and grounded language interactions. |
| Approach: | They propose to decompose and model teacher-student natural language interactions into (1) the DM’s intent to guide players toward a given goal; (2) the dm’s guidance utterance to the players expressing this intent; (3) a theory-of-mind model that anticipates the players’ reaction to the guidance one turn into the future. |
| Outcome: | The proposed task is based on a goal-driven and grounded environment with a teacher-student interaction model and theory-of-mind model. |
Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm (2025.emnlp-main)
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| Challenge: | Existing studies focus on individual quality and do not assess the value of training data. |
| Approach: | They propose a choice-based sample selection framework that evaluates sample quality . they use LLMs to evaluate the value of each option during the selection process . |
| Outcome: | The proposed model outperforms the full dataset and recent studies on a larger medical dataset. |
Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction (2025.coling-main)
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| Challenge: | Existing methods to model event associations struggle with semantic ambiguity and embedding bias. |
| Approach: | They propose a Semantic and Sentiment Dual-enhanced Generative Model to address these issues . it leverages two types of script event information to enhance the generative model . |
| Outcome: | The proposed model captures both global and local sentiments of events through its sentiment awareness mechanism. |
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing (2023.findings-acl)
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Yiqun Hu, Yiyun Zhao, Jiarong Jiang, Wuwei Lan, Henghui Zhu, Anuj Chauhan, Alexander Hanbo Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Jiang Guo, Mingwen Dong, Joseph Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang
| Challenge: | Existing text-to-SQL parsers lack the data to perform well with augmented synthetic data. |
| Approach: | They propose a framework that imposes strong typing constraints and incorporates key relationships from schema. |
| Outcome: | The proposed framework improves on the high-quality synthesized SQL and natural language question (NLQ) models have significant accuracy boosts and achieve new state-of-the-art performance on spider. |
Graph Enhanced Contrastive Learning for Radiology Findings Summarization (2022.acl-long)
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| Challenge: | Existing methods for automating impression generation have limited the relationship between extra knowledge and the original findings. |
| Approach: | They propose a framework for automating impression generation that exploits extra knowledge and original findings . they propose combining key words and their relations to extract critical information . |
| Outcome: | The proposed framework exploits extra knowledge and the original findings in an integrated way . the state-of-the-art results on two datasets confirm the effectiveness of the proposed method . |
Unsupervised Morphological Tree Tokenizer (2025.findings-acl)
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| Challenge: | Conventional statistical tokenizers often disrupt constituent boundaries within words, thereby corrupting semantic information. |
| Approach: | They propose a method that uses morphological structure guidance to induce character-level structures of words by training a deep model. |
| Outcome: | Empirical results show that the proposed method retains complete morphemes and outperforms existing methods on morphological segmentation and language modeling tasks. |
Tracing and Dissecting How LLMs Recall Factual Knowledge for Real World Questions (2025.acl-long)
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| Challenge: | Recent advances in large language models have shown promising ability to perform commonsense reasoning. |
| Approach: | They propose a two-dimensional analysis framework that incorporates token back-tracing and token decoding to uncover how LLMs conduct factual knowledge recall. |
| Outcome: | The proposed framework shows that LLMs lack relevant knowledge but struggle to select the most accurate information based on context during the retrieval and rerank phase. |
PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment (2026.findings-acl)
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| Challenge: | Existing benchmarks lack systematic approaches to integrate philosophical frameworks and expert validation for ethical reasoning assessment. |
| Approach: | They propose a philosophy-grounded approach to assess medical ethics alignment . PrinciplismQA comprises 3,648 expert-validated questions spanning knowledge assessment and clinical reasoning . |
| Outcome: | PrinciplismQA provides a philosophy-grounded approach to assessing medical ethics alignment. |
KCAT: A Knowledge-Constraint Typing Annotation Tool (P19-3)
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Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Zhigang Chen, Guoping Hu, Yueting Zhuang, Fei Wu, Xiang Ren
| Challenge: | Recent years Natural Language Processing community has seen a surge of interest in fine-grained entity typing (FET) given an entity mention (i.e. a sequence of token spans representing an entity), FET aims at uncovering its contextdependent type. |
| Approach: | They propose an efficient Knowledge Constraint Fine-grained Entity Typing Annotation Tool which further improves the entity typing process through entity linking together with some practical functions. |
| Outcome: | The proposed tool improves the entity typing process by linking the candidate types with some practical functions. |
R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling (2021.acl-long)
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| Challenge: | Existing models with stacked layers do not explicitly model hierarchical structure of language understanding. |
| Approach: | They propose a recursive Transformer model based on differentiable CKY style binary trees to emulate hierarchical composition process. |
| Outcome: | The proposed model can predict words given their left and right abstraction nodes. |
From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)
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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley
| Challenge: | Large Language Models (LLMs) have been used for selection and training of data for active learning. |
| Approach: | They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop. |
| Outcome: | The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances. |
Video-MMMU: Evaluating Knowledge Acquisition from Multidisciplinary Professional Videos (2026.acl-long)
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| Challenge: | Existing video benchmarks do not evaluate the knowledge acquisition capabilities of Large Multimodal Models (LMMs) existing video benchmark focuses on static, general visual understanding tasks, without evaluating whether models can acquire knowledge dynamically. |
| Approach: | They propose a multi-modal, multi-discipline, multitrack benchmark that evaluates Large Multimodal Models’ ability to acquire knowledge from college-level, educational videos. |
| Outcome: | The proposed benchmark reveals a substantial gap between human learners and current Large Multimodal Models (LMMs) and focuses on improving their learning efficiency. |
Improving Radiology Summarization with Radiograph and Anatomy Prompts (2023.findings-acl)
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| Challenge: | Recent studies focus on automatic impression generation, but this task is time-consuming and in high demand. |
| Approach: | They propose to use an anatomy-enhanced multimodal model to generate automatic impressions by combining radiology images with textual features. |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets and compares with existing models. |
NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models (2025.findings-acl)
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Xiang Hu, Hongyu Fu, Jinge Wang, Yifeng Wang, Zhikun Li, Renjun Xu, Yu Lu, Yaochu Jin, Lili Pan, Zhenzhong Lan
| Challenge: | Existing approaches to generate research ideas rely on retrieval or prompt engineering to generate ideas. |
| Approach: | They propose a method that uses iterative planning and search to boost creative potential of LLMs by integrating external knowledge with broader and deeper insights. |
| Outcome: | The proposed method outperforms the current state-of-the-art in generating 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation. |
HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment (2026.acl-long)
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| Challenge: | Existing methods for training reasoning-oriented large language models assume high-resource settings with abundant data. |
| Approach: | They propose a framework that integrates high-value general-domain data to promote more diverse exploration. |
| Outcome: | The proposed framework matches or surpasses RLVR trained with 32 target-domain samples using 32 target domain samples. |
Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models (2026.acl-long)
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| Challenge: | a recent study explores efficient ultra-long context modeling. |
| Approach: | They propose to use Hierarchical Sparse Attention to achieve efficient ultra-long context modeling. |
| Outcome: | The proposed model performs comparable to full-attention baselines on in-domain and out-of-domain tasks. |
Tree-Structured Non-Autoregressive Decoding for Sequence-to-Sequence Text Generation (2025.findings-emnlp)
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| Challenge: | Autoregressive Transformers suffer from high inference latency due to sequential token generation. |
| Approach: | They propose a tree-structured non-autoregressive decoding paradigm that bridges autoregressive and non-automatic decoding. |
| Outcome: | The proposed paradigm outperforms autoregressive and non-autoregressive decoding in machine translation and paraphrase generation. |
Distill, Fuse, Pre-train: Towards Effective Event Causality Identification with Commonsense-Aware Pre-trained Model (2024.lrec-main)
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| Challenge: | Existing methods to detect causal relationships in unstructured texts ignore trivial knowledge which may prejudice performance. |
| Approach: | They propose a pipeline to build a commonsense-aware pre-trained model which integrates reliable task-specific knowledge from commonsens graphs. |
| Outcome: | The proposed pipeline integrates reliable task-specific knowledge from commonsense graphs. |
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models (2025.findings-acl)
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Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu
| Challenge: | Automated Essay Scoring (AES) systems face three major challenges: reliance on handcrafted features that limit generalizability, difficulty in capturing fine-grained traits like coherence and argumentation, and inability to handle multimodal contexts. |
| Approach: | They propose a multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering. |
| Outcome: | The proposed system can evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering. |
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Existing approaches to knowledge graph entity typing ignore the way types can be clustered together. |
| Approach: | They propose a method that effectively encodes coarse-grained knowledge from clusters into entity and type embeddings. |
| Outcome: | The proposed method encodes coarse-grained knowledge from clusters into entity and type embeddings. |
Code Generation From Flowcharts with Texts: A Benchmark Dataset and An Approach (2022.findings-emnlp)
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| Challenge: | Currently, researchers focus on generating codes from requirement documents. |
| Approach: | They propose to generate source code from flowcharts with texts instead of directly translating requirements into codes. |
| Outcome: | The proposed model improves on the baselines by transforming flowcharts into pseudo-code . the proposed model is based on 320 flowchartes with their corresponding source codes . |
Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs (2025.acl-long)
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| Challenge: | Existing prompt refinement methods suffer from semantic inconsistencies and fail to maintain users’ real intent. |
| Approach: | They propose a self-instructed in-context learning framework that generates reliable derived prompts while keeping semantic consistency with original prompts. |
| Outcome: | The proposed framework generates better derived prompts and significantly enhances LLMs’ ability to deliver more effective responses. |
A Label-Aware Autoregressive Framework for Cross-Domain NER (2022.findings-naacl)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on reducing discrepancy between tokens and tokens, but transfer of valuable label information is often not considered or ignored. |
| Approach: | They propose a framework that borrows entity information from the source domain to enhance NER in the target domain. |
| Outcome: | The proposed model improves over the state-of-the-art model on several datasets. |
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction (2022.findings-naacl)
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| Challenge: | Existing methods for Few-shot Relation Extraction focus on implicitly introducing relation information to constrain the prototype representation learning. |
| Approach: | They propose a parameter-less method to promote few-shot relation extraction . they use a prototype rectification module to rectify original prototypes by relation information . |
| Outcome: | The proposed method achieves state-of-the-art on fewRel 1.0 and 2.0 datasets. |
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering (N19-1)
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| Challenge: | Existing studies have addressed this problem with partial-label loss, but they suffer from confirmation bias, which means the classifier fit a pseudo data distribution given by itself. |
| Approach: | They propose to regularize distantly supervised models with Compact Latent Space Clustering to bypass this problem and effectively utilize noisy data yet. |
| Outcome: | The proposed model outperforms state-of-the-art models on standard benchmarks on fine-grained entity typing (FET) by a significant margin. |
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction (2022.findings-acl)
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| Challenge: | Existing approaches to introduce relation information into the model are limited by labeling and data scarcity. |
| Approach: | They propose a direct addition approach to introduce relation information into a model by concatenating two views of relations and adding them to the original prototype. |
| Outcome: | The proposed approach improves on the benchmark dataset FewRel 1.0 and shows comparable results to the state-of-the-art. |
A Systematic Study of Compositional Syntactic Transformer Language Models (2025.acl-long)
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| Challenge: | Syntactic language models (SLMs) incorporate syntactical biases into Transformers . authors identify key aspects of design choices in existing models and novel variants based on experimental results . |
| Approach: | They propose a framework that incorporates existing and new SLMs to enhance Transformers by incorporating syntactic biases. |
| Outcome: | The proposed framework improves on existing models and novel variants across language modeling, syntactic generalization, summarization, and inference efficiency. |
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023.emnlp-industry)
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Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie
| Challenge: | Existing typography solutions lack adaptability, creativity, and computational efficiency. |
| Approach: | They propose a user-driven framework for artistic typography synthesis based on the Large Language Model (LLM) the LLM Engine interprets user inputs and generates actionable prompts for the other modules, transforming abstract concepts into tangible designs. |
| Outcome: | The proposed framework incorporates four key modules: the LLM Engine, SemTypo, StyTyPo, and TexTyPO. |
Transformer-based Entity Typing in Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing knowledge graphs encoding entity types are far from complete, since in real-world applications they are continuously emerging. |
| Approach: | They propose a transformer-based approach to infer plausible entity types by encoding neighbours' information by a local transformer and a global transformer. |
| Outcome: | The proposed approach outperforms the state-of-the-art on two real-world datasets. |
Chain-Talker: Chain Understanding and Rendering for Empathetic Conversational Speech Synthesis (2025.findings-acl)
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| Challenge: | Current generative CSS models face interpretability limitations due to insufficient emotional perception and redundant discrete speech coding. |
| Approach: | They propose a framework that aligns synthesized speech with the emotional context of user-agent interactions to achieve empathy. |
| Outcome: | The proposed framework produces more expressive speech than existing methods on three datasets. |
Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training (2022.findings-acl)
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| Challenge: | Existing studies treat named entity recognition as a sequential labeling problem. |
| Approach: | They propose a span selection framework for nested named entity recognition . they propose nesting entities with different input categories would be separately extracted . |
| Outcome: | The proposed framework outperforms competing models on four benchmark datasets. |
Temporal Knowledge Question Answering via Abstract Reasoning Induction (2024.acl-long)
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| Challenge: | a new method to enhance temporal knowledge reasoning in large language models addresses this challenge . Abstract Reasoning Induction (ARI) framework provides factual knowledge support to LLMs . |
| Approach: | They propose an abstract reasoning induction framework which divides temporal reasoning into two phases: Knowledge agnostic and Knowledge-based. |
| Outcome: | The proposed method achieves significant gains on two temporal QA datasets. |
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
T 2 -NER: A Two-Stage Span-Based Framework for Unified Named Entity Recognition with Templates (2023.tacl-1)
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| Challenge: | Named Entity Recognition (NER) has evolved from flat to overlapped and discontinuous . NER is a text recognition task that recognizes mentions that represent entities in text . |
| Approach: | They propose a two-stage span-based framework to solve a unified NER task using two stages . they extract entity spans, classify over all entity span pairs and combine them to train two stages. |
| Outcome: | The proposed framework beats all the current competitive baselines on eight benchmark datasets, obtaining the best performance of unified NER. |
APLOT: Robust Reward Modeling via Adaptive Preference Learning with Optimal Transport (2025.emnlp-main)
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| Challenge: | Experimental results show that RLHF improves performance of Large Language Models . BT-based RMs struggle to distinguish between similar preference responses . |
| Approach: | They propose to enhance BT-based reward models by using an adaptive margin mechanism . they use semantic similarity and reward-predicted reward differences to adjust focus . |
| Outcome: | Experimental results show that the proposed method outperforms existing methods in both in-distribution and OOD settings. |
CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID (2026.acl-long)
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| Challenge: | Existing frameworks for person re-identification fail to provide global supervision . stylistic gaps in the model can lead to shortcut learning . |
| Approach: | They propose a framework that aims to generalize a person's identity across multiple decentralized domains. |
| Outcome: | The proposed framework achieves state-of-the-art (SOTA) performance . it can generalize to unseen target environments without compromising privacy . |
Hero-Gang Neural Model For Named Entity Recognition (2022.naacl-main)
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| Challenge: | Named entity recognition (NER) is a fundamental and important task in natural language processing. |
| Approach: | They propose a novel Hero-Gang Neural structure to leverage both global and local information to promote NER by using a Transformer-based encoder and a Gang module. |
| Outcome: | The proposed model can extract local features and position information from the Hero and Gang modules, and it performs on multiple datasets. |
Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representation (2022.emnlp-main)
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| Challenge: | Chart-based models have shown great potential in unsupervised grammar induction, running recursively and hierarchically, but requiring O(n3) time-complexity. |
| Approach: | They propose a model-guided pruning method that scales to large language model pretraining by introducing a heuristic pruning method. |
| Outcome: | The proposed method significantly improves grammar induction quality and achieves competitive results in downstream tasks. |
Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)
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Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren
| Challenge: | Existing collective entity linking methods are expensive and often lack local context information. |
| Approach: | They propose a dynamic context-augmented inference model that can be used to make collective inference. |
| Outcome: | The proposed model can cope with different local EL models with different learning settings, base models, decision orders and attention mechanisms. |
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale (2024.acl-long)
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| Challenge: | Existing syntactic language models require a gold tree and sequential training to generate sentences. |
| Approach: | They propose an unsupervised syntactic language model that incrementally generates a sentence with its syntaktic tree in a left-to-right manner. |
| Outcome: | The proposed model outperforms existing models on grammar induction and comprehension tasks while holding a substantial acceleration on training. |
Beyond Itinerary Planning—A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks (2026.acl-long)
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| Challenge: | Existing studies on LLM performance on travel planning have shown that existing settings are limited due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries. |
| Approach: | They propose a benchmark to evaluate LLMs' planning and tool-use abilities in real-world settings by collecting user queries, user preferences, and tools from real scenarios. |
| Outcome: | The proposed benchmark evaluates agents' capabilities in real-world settings and shows that even advanced models exhibit imbalanced performance across different capabilities. |
Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization (2025.findings-naacl)
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Yen-Ju Lu, Ting-Yao Hu, Hema Swetha Koppula, Hadi Pouransari, Jen-Hao Rick Chang, Yin Xia, Xiang Kong, Qi Zhu, Xiaoming Simon Wang, Oncel Tuzel, Raviteja Vemulapalli
| Challenge: | Empirical results demonstrate that our method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings. |
| Approach: | They propose Mutual Reinforcing Data Synthesis (MRDS) within large language models to enhance few-shot dialogue summarization task. |
| Outcome: | Empirical results show that the proposed method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings. |
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models (2022.findings-emnlp)
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| Challenge: | Existing approaches to data-to-text generation require limited training examples . a data-based approach is based on a set of pre-trained language models with optional finetuning. |
| Approach: | They propose a data-to-text generation task that makes use of any given (or no) examples. |
| Outcome: | The proposed approach improves on baselines on a dataset with zero/few/full-shot settings. |
Interactive Question Clarification in Dialogue via Reinforcement Learning (2020.coling-industry)
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| Challenge: | ambiguous questions are a perennial problem in real-world dialogue systems. |
| Approach: | They propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query. |
| Outcome: | The proposed model improves on real-world user clicks and shows significant improvements . it suggests that the original query is refined to clarify ambiguous questions . |
LongGenBench: Long-context Generation Benchmark (2024.findings-emnlp)
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| Challenge: | Current long-context benchmarks focus on retrieval-based tests, requiring Large Language Models to locate specific information within extensive input contexts. |
| Approach: | They propose a long-context generation benchmark that allows for flexible configurations of customized generation context lengths. |
| Outcome: | The proposed benchmark improves performance on NIAH and other retrieval-based tests. |
Few-Shot Charge Prediction with Discriminative Legal Attributes (C18-1)
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| Challenge: | Existing works on charge prediction perform well on high-frequency charges but are not capable of predicting few-shot charges with limited cases. |
| Approach: | They propose an attribute-attentive charge prediction model to infer attributes and charges simultaneously . they propose to use discriminative attributes as the internal mapping between fact descriptions and charges . |
| Outcome: | The proposed model outperforms baseline models on real-world datasets by more than 50% . the proposed model can predict the attributes and charges simultaneously . |
Improving Grammatical Error Correction with Multimodal Feature Integration (2023.findings-acl)
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| Challenge: | Experimental results show that multimodal GEC models improve over strong baselines and achieve a new state-of-the-art result on the Falko-MERLIN test set. |
| Approach: | They propose a framework that integrates both speech and text features to enhance GEC by generating audio from text using advanced text-to-speech models. |
| Outcome: | The proposed framework improves on CoNLL14, BEA19 English, and Falko-MERLIN German datasets. |
Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity (2025.findings-emnlp)
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Ping Chen, Xiang Liu, Zhaoxiang Liu, Zezhou Chen, Xingpeng Zhang, Huan Hu, Zipeng Wang, Kai Wang, Shuming Shi, Shiguo Lian
| Challenge: | ambiguity, polysemy, or uncertainty remain significant challenges in natural language processing. |
| Approach: | They introduce a framework that integrates LLM semantic priors with continuous fuzzy membership degrees to create an explicit interaction between probability-based reasoning and fuzzy membership reasoning. |
| Outcome: | The proposed framework integrates semantic priors with continuous fuzzy membership degrees . it allows ambiguous inputs to be gradually transformed into clear and interpretable decisions . |