Papers by Jing Gao

Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)

Challenge:	Retrieval-augmented Large Language Models struggle with complex inputs and noisy knowledge retrieval hindering model effectiveness.
Approach:	They propose a query generation method that integrates query generation blending with knowledge filtering to enhance retrieval-augmented LLMs.
Outcome:	The proposed approach surpasses state-of-the-art benchmarks on open-domain question answering benchmarks.

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Yaqing Wang, Haoda Chu, Chao Zhang, Jing Gao

Low-Resource NLP

Challenge:	Named entity recognition (NER) is a language understanding task that requires large amounts of in-domain labeled data to perform well.
Approach:	They propose a framework which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data.
Outcome:	The proposed method brings 10%, 23% and 26% improvements over baselines in few-shot learning, domain transfer and zero-shot settings respectively.

FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning (2026.findings-acl)

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Yuxi Sun, Aoqi Zuo, Haotian Xie, Wei Gao, Mingming Gong, Jing Ma

LA-UCL: LLM-Augmented Unsupervised Contrastive Learning Framework for Few-Shot Text Classification (2024.lrec-main)

Challenge:	Existing models generate explanations that appear coherent while containing unfaithful intermediate steps.
Approach:	They propose a causality-inspired framework for evaluating CoT quality using controlled perturbations as an instrumental signal to separate genuine step-to-step dependence from bias-driven artifacts.
Outcome:	Experiments on GSM8K, MATH, and CommonsenseQA show that FACT-E improves reasoning-trajectory selection and yields stronger in-context learning exemplars.

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Jing Zhang, Hui Gao, Peng Zhang, Boda Feng, Wenmin Deng, Yuexian Hou

MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG (2025.findings-naacl)

Challenge:	Experimental results show that our model exceeds the baseline models due to the lack of cognitive ability.
Approach:	They propose a LLM-Augmented Unsupervised Contrastive Learning Framework which introduces a cognition-enabled Large Language Model (LLM) for efficient data augmentation and presents corresponding contrastive learning strategies.
Outcome:	The proposed model exceeds baseline models on six datasets.

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Pingyu Wu, Daiheng Gao, Jing Tang, Huimin Chen, Wenbo Zhou, Weiming Zhang, Nenghai Yu

The CRINGE Loss: Learning what language not to model (2023.acl-long)

Challenge:	Existing approaches to retrieve entity information are limited by document level retrieval and intermingled storage of information from different entities.
Approach:	They propose a framework that enhances entity-specific query handling . MES-RAG introduces proactive security measures that ensure system integrity .
Outcome:	Experimental results show that MES-RAG improves accuracy and recall . the framework can be integrated into existing RAG architectures .

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Leonard Adolphs, Tianyu Gao, Jing Xu, Kurt Shuster, Sainbayar Sukhbaatar, Jason Weston

Challenge:	Standard language model training uses gold human documents or human-human interaction data and treats all training data as positive examples.
Approach:	They propose a procedure to train with negative examples using the "CRINGE" loss technique and use it to train models with such data.
Outcome:	The proposed procedure outperforms multiple strong baselines and is simple to train and implement.

CoT-VTM: Visual-to-Music Generation with Chain-of-Thought Reasoning (2025.findings-acl)

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Xikang Guan, Zheng Gu, Jing Huo, Tianyu Ding, Yang Gao

MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)

Challenge:	Existing methods for visual-to-music generation lack large-scale, high-quality visual-music paired datasets and lack of direct semantic correspondence between visuals and music.
Approach:	They propose a framework that distills Chain-of-Thought reasoning to enable visual-to-music generation without paired data.
Outcome:	The proposed framework achieves optimal performance on image-to-music and video-to music tasks.

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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen

WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom (2023.emnlp-main)

Challenge:	Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed .
Approach:	They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities.
Outcome:	The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech .

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Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, Zhiwei Yang

Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks (P19-1)

Challenge:	Existing methods for debunking fake news rely on blending of authentic and fabricated content by creators.
Approach:	They propose a model that detects misinformation at sentence-level using social media conversations . they use a bag-level annotation system to train the model .
Outcome:	The proposed model outperforms existing state-of-the-art models on three real-world benchmarks and outperformed existing state of the art models in debunking fake news at sentence and article levels.

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Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong

Dialectical Structured Reasoning for Explainable Multimodal Fake News Detection (2026.findings-acl)

Challenge:	Claim verification is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence.
Approach:	They propose an end-to-end hierarchical attention network that learns to represent coherent evidence and their semantic relatedness with the claim.
Outcome:	The proposed model outperforms state-of-the-art models on three datasets . it is based on a coherence-based attention layer and entailment-based one .

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Ruichao Yang, Yufan Bian, Wei Gao, Bo-Wen Zhang, Jing Ma, Hongzhan Lin, Ziyang Luo, Xiaobin Zhu, Xu-Cheng Yin

REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control (2026.acl-long)

Challenge:	Existing fake news detection models are opaque and lack deductive transparency . a framework for dialectical structured reasoning is proposed to address this limitation .
Approach:	They propose a framework that model fake news detection as an explicit dialectical process over multimodal social context.
Outcome:	The proposed framework achieves state-of-the-art while producing transparent explanations that mirror human reasoning process.

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Chuyi Kong, Wei Gao, Jing Ma, Hongzhan Lin, Yuxi Sun

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

Challenge:	Existing methods for automated fact-checking often overlook deceptive misinformation styles in generated explanations.
Approach:	They propose a framework that explicitly controls reasoning style by anchoring explanations to the predicted verdict.
Outcome:	The proposed framework achieves state-of-the-art under LLaMA-series models with 465 samples.

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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun

HadSkip: Homotopic and Adaptive Layer Skipping of Pre-trained Language Models for Efficient Inference (2023.findings-emnlp)

Challenge:	Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages .
Approach:	They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder .
Outcome:	The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities.

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Haoyu Wang, Yaqing Wang, Tianci Liu, Tuo Zhao, Jing Gao

Knowledge-Guided Paraphrase Identification (2021.findings-emnlp)

Challenge:	Existing methods to exit pre-trained language models suffer from the limitation that they have to sequentially traverse through all layers prior to the selected exit layer, which degrades their performance.
Approach:	They propose a homotopic and adaptive layer skipping fine-tuning method that adaptively selects the layers to skip based on a predefined budget.
Outcome:	The proposed method outperforms all state-of-the-art baselines on the GLUE benchmark and shows that it is highly efficient.

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Haoyu Wang, Fenglong Ma, Yaqing Wang, Jing Gao

Text Generation

Challenge:	Existing methods for paraphrase identification (PI) are limited due to lack of professional knowledge.
Approach:	They propose to leverage Wikipedia knowledge to accurately identify paraphrases by mining outline knowledge of given sentences from Wikipedia.
Outcome:	The proposed framework outperforms state-of-the-art models on two public datasets: PARADE and clinicalSTS2019.

UICOMPASS: UI Map Guided Mobile Task Automation via Adaptive Action Generation (2025.emnlp-main)

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Yuanzhang Lin, Zhe Zhang, He Rui, Qingao Dong, Mingyi Zhou, Jing Zhang, Xiang Gao, Hailong Sun

Challenge:	Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users’ commands on mobile devices like Android.
Approach:	They propose a UI Map-guided LLM-based approach to automate mobile tasks using static analysis and LLMs.
Outcome:	The proposed approach achieves a 15.87% higher task execution success rate than SOTA approaches even when only APK is available.

Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (N19-1)

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Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang

Reasoning

Challenge:	Existing methods for commonsense reasoning rely on human-crafted features and knowledge bases, but unsupervised learning is not feasible due to the lack of labeled training data or comprehensive knowledge bases.
Approach:	They propose two unsupervised models based on the Deep Structured Semantic Models framework to tackle two commonsense reasoning tasks: Winograd Schema Challenge (WSC) and Pronoun Disambiguation (PDP).
Outcome:	The proposed models capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.

Towards Universal Debiasing for Language Models-based Tabular Data Generation (2025.findings-emnlp)

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Tianchun Li, Tianci Liu, Xingchen Wang, Rongzhe Wei, Pan Li, Lu Su, Jing Gao

Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (P18-1)

Challenge:	Existing large language models have exacerbated fairness issues in tabular data generation . inherent historical biases in tabulated data cause LLMs to exacerbate fairness problems .
Approach:	They propose a universal debiasing framework that minimizes group-level dependencies . it leverages the autoregressive structure and analytic sampling distributions of LLM-based tabular data generators .
Outcome:	The proposed framework minimizes group-level dependencies while reducing mutual information between advantaged and protected attributes.

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Jing Ma, Wei Gao, Kam-Fai Wong

Challenge:	Existing methods for detecting rumors are difficult to implement and require a lot of effort.
Approach:	They propose two recursive neural models that follow tweets' propagation layouts to learn discriminative features from tweets and generate more powerful representations for rumors detection.
Outcome:	The proposed models perform better than state-of-the-art approaches on two public Twitter datasets and show superior performance on detecting rumors at very early stage.

Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation (2026.acl-long)

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Jiawei Zhou, Chi Zhang, Xiang Feng, Qiming Zhang, Haibo Qiu, Lihuo He, Dengpan Ye, Xinbo Gao, Jing Zhang

ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation (2025.findings-acl)

Challenge:	a benchmark is designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured digital graphics into executable code.
Approach:	They propose a benchmark to evaluate the capability of Large Multimodal Models to convert digital graphics into executable code.
Outcome:	The proposed benchmark exposes the performance gap among leading LMMs . the benchmark features 1130 meticulously curated samples .

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Kaiyuan Liu, Youcheng Pan, Yang Xiang, Daojing He, Jing Li, Yexing Du, Tianrun Gao

Programming Languages in NLP

Challenge:	Existing benchmarks lack the ability to automatically evaluate from users’ perspective and lack the explainability of the results of LLM agents’ code generation capabilities.
Approach:	They propose a new benchmark for LLM agents' automated evaluation by simulating user interaction.
Outcome:	The proposed benchmark can evaluate the generated projects by user interaction simulation and by code similarity through existing objective indicators.

SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation (2024.emnlp-main)

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Xiaoze Liu, Ting Sun, Tianyang Xu, Feijie Wu, Cunxiang Wang, Xiaoqian Wang, Jing Gao

Robustness in NLP

Challenge:	Large Language Models (LLMs) have transformed machine learning but have raised significant legal concerns due to their potential to produce text that infringes on copyrights.
Approach:	They propose a lightweight, real-time defense mechanism to prevent the generation of copyrighted text by evaluating methods and testing attack strategies.
Outcome:	The proposed defense significantly reduces the volume of copyrighted text generated by LLMs by effectively refusing malicious requests.

OASIS: Order-Augmented Strategy for Improved Code Search (2025.acl-long)

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Gao Zuchen, Zizheng Zhan, Xianming Li, Erxin Yu, Haotian Zhang, Chenbin Chenbin, Yuqun Zhang, Jing Li

SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales (2024.emnlp-main)

Challenge:	Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications.
Approach:	They propose an order-augmented strategy for improved code search that leverages order-based similarity labels to capture subtle differences in similarity among negative pairs.
Outcome:	The proposed model outperforms state-of-the-art models focusing on major positive-negative differences.

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Tianyang Xu, Shujin Wu, Shizhe Diao, Xiaoze Liu, Xingyao Wang, Yangyi Chen, Jing Gao

RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning (2024.emnlp-main)

Challenge:	Existing approaches to elicit confidence from large language models are limited to binary or inaccurate group-level confidence estimates.
Approach:	They propose a training framework that teaches LLMs to express more fine-grained confidence estimates.
Outcome:	The proposed training framework reduces the confidence calibration error and maintains the performance of the model.

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Haoyu Wang, Tianci Liu, Ruirui Li, Monica Cheng, Tuo Zhao, Jing Gao

Rethink Rumor Detection in the Era of LLMs: A Review (2025.findings-emnlp)

Challenge:	Pre-trained language models have strong generalizability, but fine-tuning involves updating all parameters, rendering full fine-uning resource-intensive.
Approach:	They propose a parameter-efficient fine-tuning method that updates all pre-trained parameters during inference.
Outcome:	The proposed method outperforms baseline methods on five benchmarks across 20 datasets.

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Chang Yang, Peng Zhang, Jing Zhang, Hui Gao, Changhao Song

CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference (2024.emnlp-main)

Challenge:	rumor detection has been reshaped by large language models (LLMs) this paper proposes a Cognition-Interaction-Behavior (CIB) framework for rumour detection based on collective intelligence .
Approach:	They propose a Cognition-Interaction-Behavior framework for rumor detection based on collective intelligence and explore synergistic relationship between LLMs and collective intelligence in rumour governance.
Outcome:	The proposed framework unifies existing methods and reveals synergistic relationship between LLMs and collective intelligence in rumor governance.

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Erxin Yu, Jing Li, Ming Liao, Siqi Wang, Gao Zuchen, Fei Mi, Lanqing Hong

Robustness in NLP

Challenge:	Existing studies have not noticed the safety risks of large language models . authors evaluated 1,400 questions in multi-turn dialogue coreference .
Approach:	They are the first to evaluate LLM safety in multi-turn dialogue coreference . they created a dataset of 1,400 questions and tested five open-source models .
Outcome:	The study shows that model safety decreases in multi-turn dialogue coreference scenarios . the highest success rate was with the LLaMA2-Chat-7b model, while the lowest was with mistral-7B-Instruct model .

MDTeamGPT: Mitigating Context Collapse and Enabling Self-Evolution in Medical Multi-Agent Reasoning (2026.findings-acl)

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Kai Chen, Xinfeng Li, Tianpei Yang, Hewei Wang, Guang Yang, Jing Huo, Yang Gao

Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning (2026.acl-long)

Challenge:	Long, multi-round, multirole interaction trajectories lead to severe information dilution and context window overload, triggering context collapse which destabilizes reasoning.
Approach:	They propose a multi-agent framework that compresses and reorganizes multi-round consensus.
Outcome:	The proposed framework outperforms baselines across text-based and multimodal tasks while demonstrating superior diagnostic performance and stability in complex clinical scenarios.

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Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao

Reasoning

Challenge:	Existing work on large reasoning models (LRMs) focuses on using reinforcement learning (RL) to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query.
Approach:	They propose to use reinforcement learning to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query.
Outcome:	The proposed model reduces token usage by around 50%$ compared to DeepSeek-R1-Distill-Qwen-1.5B/7B and DeepScaleR-1.5b, while significantly improving accuracy.

Deciphering Rumors: A Multi-Task Learning Approach with Intent-aware Hierarchical Contrastive Learning (2024.emnlp-main)

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Chang Yang, Peng Zhang, Hui Gao, Jing Zhang

LLM-SLM Collaborative Framework of Idiomatic Expression Generation (2026.acl-long)

Challenge:	Social networks are rife with noise and misleading information, presenting multifaceted challenges for rumor detection.
Approach:	They propose a new multi-task learning framework that mines latent intentions and rumor semantic features . they propose to use event-level and intent-level strategies to establish cognitive anchors .
Outcome:	The proposed framework improves the effectiveness of rumor detection and addresses the challenges present in the field.

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Hui Gao, Changhao Song, Peng Zhang, Jing Zhang, Chang Yang, Liuxian Ge

LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models (2026.acl-industry)

Challenge:	Existing methods for idiomatic expression generation lack parallel data and manual annotations.
Approach:	They propose an iterative LLM-SLM collaborative framework that replaces human supervision for idiomatic expression data generation.
Outcome:	The proposed framework outperforms DeepSeek-R1 in Chinese Idiom Polishing with a 25.2% improvement in accuracy.

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Tianchun Li, Haochen Liu, Vishwa Pardeshi, Xingchen Wang, Tianci Liu, Huijun Zhao, Wei Fan, Jing Gao

Weighted Contrastive Learning With False Negative Control to Help Long-tailed Product Classification (2023.acl-industry)

Challenge:	Small language models (SLMs) are promising for real-world deployment but struggle with high-stakes legal reasoning tasks.
Approach:	They propose a diagnostic-driven synthesis framework that extracts and refines reasoning trajectories from a capable teacher via fine-grained prompting and a self-reflective verification is employed to adaptively select the most effective data for the SLM student.
Outcome:	The proposed framework extracts and refines reasoning trajectories from a capable teacher via fine-grained prompting, then a self-reflective verification is employed to adaptively select the most effective data for the student.

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Tianqi Wang, Lei Chen, Xiaodan Zhu, Younghun Lee, Jing Gao

Topic Memory Networks for Short Text Classification (D18-1)

Challenge:	Item categorization (IC) aims to classify a product into leaf nodes in a categorical taxonomy due to scarce supervision.
Approach:	They propose to use K-positive contrastive loss (KCL) to address IC task’s long-tail issue by re-weighting positive pairs in the KCL loss with a regularization that the sum of weights should be constrained to K+1 as close as possible.
Outcome:	The proposed method improves on the long-tail issue in the image classification task and when using text-based contrastive learning, it can be applied on the IC task.

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Jichuan Zeng, Jing Li, Yan Song, Cuiyun Gao, Michael R. Lyu, Irwin King

Information Extraction & Text Mining

Challenge:	Existing classification models for short texts are weak due to data sparsity .
Approach:	They propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels.
Outcome:	The proposed model outperforms state-of-the-art models on short text classification, while generating coherent topics.

CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion (2024.findings-acl)

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Qibing Ren, Chang Gao, Jing Shao, Junchi Yan, Xin Tan, Wai Lam, Lizhuang Ma

Programming Languages in NLP

Challenge:	Large Language Models exhibit remarkable generative capabilities but can be misused for harmful purposes.
Approach:	They propose a framework that transforms natural language inputs into code inputs.
Outcome:	The proposed framework bypasses the safety guardrails of all models more than 80% of the time.

Macedon: Minimizing Representation Coding Rate Reduction for Cross-Lingual Natural Language Understanding (2023.findings-emnlp)

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Haoyu Wang, Yaqing Wang, Huaxiu Yao, Jing Gao

Multilinguality

Challenge:	Existing approaches to learn cross-lingual models require limited data to perform cross-linguistic tasks.
Approach:	They propose a method to remove language-associated information via minimizing representation coding rate reduction.
Outcome:	The proposed model outperforms state-of-the-art models on cross-lingual tasks.

An Interpretable and Crosslingual Method for Evaluating Second-Language Dialogues (2025.naacl-long)

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Rena Wei Gao, Xuetong Wu, Carsten Roever, Jing Wu, Long Lv, Jingxuan Wu, Jey Han Lau

Dialogue Systems & Conversational Agents

Challenge:	Existing studies on second language (SL) assessment of conversational fluency and interactivity have focused on written correction or pronunciation from ASR.
Approach:	They propose a framework that assesses the relationships between micro-level linguistic features and macro-level interactivity labels for Chinese-as-a-second-language dialogues.
Outcome:	The proposed framework is interpretable and can be adapted to other languages for second-language dialogue evaluation.

Debunking Rumors on Twitter with Tree Transformer (2020.coling-main)

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Jing Ma, Wei Gao

AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning (2022.emnlp-main)

Challenge:	Existing methods for rumor detection follow tree edges or treat all posts fully-connected during feature learning.
Approach:	They propose a new rumor detection model based on tree transformer to better utilize user interactions in the dialogue . they propose to use post-level self-attention to aggregate the intra-/inter-subtree stances .
Outcome:	The proposed model improves rumor detection performance on social media conversations . it is based on a conversation tree that encodes important information indicative of credibility .

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Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao

Self-Adjust Softmax (2025.emnlp-main)

Challenge:	Standard fine-tuning of large pre-trained language models requires updating hundreds of millions to billions of parameters and storing a large copy of the PLM weights for every task.
Approach:	They propose a parameter-efficient fine-tuning technique where small trainable components are injected into the PLM and updated during fine-uning.
Outcome:	The proposed method outperforms SOTA parameter-efficient fine-tuning and full model fine-uning on GLUE development set with RoBERTa-large encoder.

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Chuanyang Zheng, Yihang Gao, Guoxuan Chen, Han Shi, Jing Xiong, Xiaozhe Ren, Chao Huang, Zhenguo Li, Yu Li

Consistency Rating of Semantic Transparency: an Evaluation Method for Metaphor Competence in Idiom Understanding Tasks (2025.coling-main)

Challenge:	Usually, tokens with larger attention scores are important for the final prediction.
Approach:	They propose to modify softmax(z) to z softmax and its normalized variant to improve the Transformer attention mechanism by making minor adjustments to the softmax function.
Outcome:	The proposed model provides enhanced gradient properties compared to the vanilla softmax function.

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Hui Gao, Jing Zhang, Peng Zhang, Chang Yang

CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention (2025.findings-acl)

Challenge:	Idioms condense complex semantics into fixed phrases, making idiom comprehension a test of metaphor competence.
Approach:	They propose a method to evaluate the metaphor competence of LLMs for the idiom understanding task: the Consistency Rating of Semantic Transparency (CR-ST).
Outcome:	The proposed method assesses the difficulty of understanding idioms through two dimensions: overall semantic transparency and constituent semantic transparency, aiming to gauge LLMs’ mastery of metaphor competence.

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Yuxi Sun, Aoqi Zuo, Wei Gao, Jing Ma

Distilling Causal Effect of Data in Continual Few-shot Relation Learning (2024.lrec-main)

Challenge:	Existing methods to reduce hallucinations in large language models are inaccurate and inaccuracies in the generated feedback.
Approach:	They propose a method that helps LLMs determine whether to utilize multiple generated feedback responses and how to identify the most useful ones.
Outcome:	Extensive experiments show that the proposed method outperforms baselines on encyclopedic and commonsense knowledge QA tasks.

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Weihang Ye, Peng Zhang, Jing Zhang, Hui Gao, Moyao Wang

Context-Situated Pun Generation (2022.emnlp-main)

Challenge:	Existing methods for learning relational patterns from data are prone to catastrophic forgetting issues due to limited number of samples and continual training mode.
Approach:	They propose a unified causal framework for CFRL to restore causal effects from old data . they establish two additional causal paths from old to predictions by colliding with old data separately in the old feature space.
Outcome:	The proposed method is superior to existing state-of-the-art methods in CFRL task settings.

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Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Shuyang Gao, Tagyoung Chung, Jing Huang, Yang Liu, Nanyun Peng

Information Retrieval

Challenge:	a new task for context-situated pun generation uses a given context to generate puns . human evaluation shows that 69% of top retrieved pun words can be used to generate context-based puns.
Approach:	They propose a task where puns are generated based on contextual keywords and pun words.
Outcome:	The proposed system generates successful puns 31% of the time given a plausible tuple of context words and pun pairs.

LiST: Lite Prompted Self-training Makes Parameter-efficient Few-shot Learners (2022.findings-naacl)

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Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Awadallah, Jianfeng Gao

DAPE V2: Process Attention Score as Feature Map for Length Extrapolation (2025.acl-long)

Challenge:	LiST is an efficient method for fine-tuning large pre-trained language models in few-shot learning settings.
Approach:	They propose a method for efficient fine-tuning of large pre-trained language models in few-shot settings using self-training and meta-learning.
Outcome:	The proposed method outperforms GPT-3 in-context learning by 33% on few-shot tasks.

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Chuanyang Zheng, Yihang Gao, Han Shi, Jing Xiong, Jiankai Sun, Jingyao Li, Minbin Huang, Xiaozhe Ren, Michael Ng, Xin Jiang, Zhenguo Li, Yu Li

Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness (2021.emnlp-main)

Challenge:	Extensive experiments demonstrate that treating attention as a feature map and applying convolution as . a processing method significantly enhances Transformer performance.
Approach:	They propose to use the convolution operator to mimic the processing methods in computer vision to treat attention as a feature map and apply it to neighboring attention scores across different heads.
Outcome:	The proposed model can be adapted to various attention-related models and achieves high performance.

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Hengtong Zhang, Tianhang Zheng, Yaliang Li, Jing Gao, Lu Su, Bo Li

Robustness in NLP

Challenge:	a recent study shows that inappropriate language can cause models to output profanity . authors propose a training framework to prevent such outputs from hurting the usability of models .
Approach:	proposed training framework eliminates the causes that trigger the generation of profanity . authors propose a framework that leverages a short list of profans to prevent this .
Outcome:	a proposed training framework can prevent models from generating profanity . the proposed framework leverages a short list of profanities examples .

Multimodal Machine Translation with Text-Image In-depth Questioning (2025.findings-acl)

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Yue Gao, Jing Zhao, Shiliang Sun, Xiaosong Qiao, Tengfei Song, Hao Yang