Papers by Yi Jing
Soft Language Clustering for Multilingual Model Pre-training (2023.acl-long)
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| Challenge: | Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from the source language or when pre-training data is limited in size. |
| Approach: | They propose a method that contextually retrieves prompts as flexible guidance for encoding instances conditionally. |
| Outcome: | The proposed method improves on the XTREME task and also for low-resource languages in unsupervised sentence retrieval. |
LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder (2025.emnlp-main)
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| Challenge: | Prior research on linguistic mechanisms of large language models is limited by coarse granularity, limited analysis scale, and narrow focus. |
| Approach: | They propose a framework for analyzing the linguistic mechanisms of large language models based on Sparse Auto-Encoders. |
| Outcome: | The proposed framework extracts Chinese and English linguistic features across four dimensions . it uncovers intrinsic representations of linguistic knowledge in LLMs and can control outputs . |
Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline (2025.findings-emnlp)
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| Challenge: | Existing retrieval methods neglect the execution sequence structures inherent in procedural documents. |
| Approach: | They propose a retrieval model which integrates procedural graphs with document representations. |
| Outcome: | The proposed model integrates procedural graphs with document representations to improve document retrieval. |
Automated Progressive Red Teaming (2025.coling-main)
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| Challenge: | Automated red teaming (ART) is effective but time-consuming, costly and lacks scalability. |
| Approach: | They propose an automated red teaming framework that generates adversarial prompts to expose LLM vulnerabilities. |
| Outcome: | The proposed framework explores and exploits LLM vulnerabilities through multi-round interactions. |
SafetyQuizzer: Timely and Dynamic Evaluation on the Safety of LLMs (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have been used to evaluate the safety of their users . however, evaluation questions in current benchmarks are too straightforward and difficult to update with practical relevance due to their lack of correlation with real-world events. |
| Approach: | They propose a question-generation framework to evaluate the safety of LLMs in the Chinese context. |
| Outcome: | The proposed framework reduces decline rate while maintaining similar attack success rate. |
Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)
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| Challenge: | Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found. |
| Approach: | They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path. |
| Outcome: | The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge. |
Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)
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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available. |
| Approach: | They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs. |
| Outcome: | The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer. |
CoreGaze: Core Subgraph-Driven Visual Gaze Diffusion for Training-Free Referring Multimodal Large Language Models (2026.acl-long)
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| Challenge: | Existing methods rely on extensive fine-tuning to mitigate attention distraction, leading to redundant outputs or hallucinations. |
| Approach: | They propose a training-free framework that simulates human visual gaze diffusion for fine-grained comprehension by combining a sparse semantic graph with a core subgraph with amplified initial influence. |
| Outcome: | The proposed framework simulates human visual gaze diffusion for fine-grained comprehension. |
Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment (2026.acl-long)
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Bryan Chen Zhengyu Tan, Zhengyuan Liu, Xiaoyuan Yi, Jing Yao, Xing Xie, Nancy F. Chen, Roy Ka-Wei Lee
| Challenge: | Current alignment paradigms treat "human values" as a monolithic entity, ignoring the fact that many societies are a mosaic of diverse subgroups with distinct and sometimes conflicting values, preferences, and norms. |
| Approach: | They examine whether Large Language Models can emulate distinct cultural values of subgroups . they use a global value survey to examine the value landscape of a multicultural society . |
| Outcome: | The proposed model improves on unseen, out-of-distribution subgroups by 17.4% . the model widens the disparity between subgroup groups when measured by distance-aware metrics. |
PsyChain: A Collaborative Chain-of-Agents Framework for Generating Personalized and Professional Counseling Dialogues (2026.findings-acl)
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| Challenge: | Existing psychological counseling datasets suffer from monolithic client personas, insufficient therapeutic depth, and a lack of process controllability. |
| Approach: | They propose a framework that evolves static counseling corpora into high-fidelity dialogues . they use a Client Profiler that pairs life scenarios with psychological personality archetypes based on client personality and stage progression . |
| Outcome: | The proposed framework achieves 61-91% win rates against domain-specific baselines in pairwise evaluation and the highest average score in human evaluation, indicating potential for real-world counseling. |
Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection (2023.emnlp-main)
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| Challenge: | Existing keyphrase extraction models incorrectly determine a keyphrase as a phrase but output other candidates as keyphrases because they contain the same word. |
| Approach: | They propose a new approach that detects both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate keyphrase. |
| Outcome: | The proposed approach outperforms state-of-the-art keyphrase extraction models on three benchmark datasets. |
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. |
Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models (2025.emnlp-main)
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Yi Feng, Jiaqi Wang, Wenxuan Zhang, Zhuang Chen, Shen Yutong, Xiyao Xiao, Minlie Huang, Liping Jing, Jian Yu
| Challenge: | Existing approaches to mental health support lack realism and capture therapeutic progression over time. |
| Approach: | They propose a framework that simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate responses through retrieval-augmentation. |
| Outcome: | The proposed framework outperforms standard methods in quality and depth on 260 simulated clients and 230 human participants. |
Unintended Harms of Value-Aligned LLMs: Psychological and Empirical Insights (2025.acl-long)
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| Challenge: | Value-aligned LLMs are more prone to harmful behavior than fine-tuned models . value-aligned models generate text according to the aligned values, which can amplify harmful outcomes. |
| Approach: | They propose to use in-context alignment methods to enhance the safety of value-aligned LLMs. |
| Outcome: | The proposed methods improve value alignment and safety, the authors say . value-aligned models are more prone to harmful behavior than fine-tuned models . |
Beyond Excess and Deficiency: Adaptive Length Bias Mitigation in Reward Models for RLHF (2025.findings-naacl)
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| Challenge: | Existing efforts to mitigate length bias in reward models have decreased accuracy . achieving an automatic proxy that perfectly replicates human judgment is challenging in practice. |
| Approach: | They propose an adaptive approach that dynamically adjusts the influence of response length in reward evaluations according to the context of the query. |
| Outcome: | The proposed approach reduces unnecessary verbosity while improving overall response quality. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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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
| 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 . |
PRDetect: Perturbation-Robust LLM-generated Text Detection Based on Syntax Tree (2025.findings-naacl)
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| Challenge: | Recent methods for detecting LLM-generated text have shown impressive performance, but in real-world scenarios, users often introduce perturbations to the text. |
| Approach: | They propose a method that detects syntactic trees that are minimally affected by perturbations and exhibit distinct differences between human-written and LLM-generated text. |
| Outcome: | The proposed method shows that it is significantly better against perturbations on the HC3 and GPT-3.5-mixed datasets and also has the shortest time expenditure. |
EvoBench: Towards Real-world LLM-Generated Text Detection Benchmarking for Evolving Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods to detect LLM-generated texts rely on static benchmarks that neglect the evolving nature of LLMs. |
| Approach: | They propose a benchmark to evaluate the generalization of LLM-generated text detection methods. |
| Outcome: | The proposed benchmark measures generalization of 14 detection methods across LLMs. |
HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Existing studies for named entity recognition focus on flat NER, i.e., without nested entities, by sequence labeling methods. |
| Approach: | They propose a Hierarchical Transformer network which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner. |
| Outcome: | The proposed method achieves much better performance than the state-of-the-art approaches on GENIA, ACE-2004, ace-2005 and NNE datasets. |
TranSFormer: Slow-Fast Transformer for Machine Translation (2023.findings-acl)
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| Challenge: | Prior work has focused on treating subwords as basic units in developing such systems. |
| Approach: | They propose a slow-fast two-stream learning model that uses a “slow” branch to deal with subword sequences and a "fast" branch to cope with longer character sequences. |
| Outcome: | The proposed model shows consistent BLEU improvements (larger than 1 BLUE point) on several machine translation benchmarks. |
Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation (2025.acl-demo)
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Jing Yao, Xiaoyuan Yi, Shitong Duan, Jindong Wang, Yuzhuo Bai, Muhua Huang, Yang Ou, Scarlett Li, Peng Zhang, Tun Lu, Zhicheng Dou, Maosong Sun, James Evans, Xing Xie
| Challenge: | Current evaluation methods for large language models face two key challenges: 1. evaluation validity and 2. Result interpretation reduce the pluralistic and incommensurable values to one-dimensional scores. |
| Approach: | They propose a platform for comprehensive value diagnosis of large language models (LLMs) that provides a generative evaluation paradigm that automatically creates real-world test items co-evolving with ever-advancing LLMs. |
| Outcome: | The proposed platform provides a framework for comprehensive value diagnosis of large language models (LLMs) with fine-grained scores and case studies across 27 value dimensions for 33 leading LLMs, customized comparisons, and visualized analysis of LLM’s alignment with cultural values. |
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)
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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang
| Challenge: | Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). |
| Approach: | They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE. |
| Outcome: | The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency. |
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods for fake news detection focus on fact-checked reports, resulting in limited coverage and debunking delays. |
| Approach: | They propose a Coarse-to-fine Cascaded Evidence-Distillation neural network for explainable fake news detection based on raw reports . they use hierarchical encoders and cascaded selectors to select most explainable sentences for verdicts on top of selected top-K reports based upon raw reports. |
| Outcome: | The proposed model outperforms baseline detection methods and generates high-quality explanations from diverse evaluation perspectives. |
Match More, Extract Better! Hybrid Matching Model for Open Domain Web Keyphrase Extraction (2024.findings-acl)
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| Challenge: | Existing models for keyphrase extraction use noisy information to filter the salient phrases from the document. |
| Approach: | They propose a hybrid matching model that combines representation-focused and interaction-based matching modules into a unified framework for improving keyphrase extraction. |
| Outcome: | The proposed model outperforms state-of-the-art keyphrase extraction models on the OpenKP dataset. |
Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Value (2024.naacl-long)
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| Challenge: | Existing work specifies values as risk criteria formulated in the AI community, e.g., fairness and privacy protection, suffering from poor clarity, adaptability and transparency. |
| Approach: | They propose a value alignment paradigm based on Schwartz's Theory of Basic Values as an instantiation and propose 'BaseAlign' to support this paradigm. |
| Outcome: | The proposed model covers existing risks and anticipates unidentified ones with a low-data set. |
Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework (2026.acl-industry)
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Ziqiang Zhang, Jing Ma, Zilong Wang, Jiayuan Chen, Yi Qiao, Yu He, Wei Zhang, Dai Cheng, Xiaoyu Shen
| Challenge: | a production-grade pricing system for tourism is challenging due to unstructured nature of travel orders and ever-evolving pricing policies. |
| Approach: | They propose a production-grade pricing system with a strict decision boundary . they propose to combine structured extraction and bounded policy/path selection with interpretable condition trees . |
| Outcome: | The proposed system processed 3,960 orders in six months and reduced the order management team from 15-20 to 3 . the system reduced the per-order handling time from 10 minutes to 2 minutes. |
MoVa: Towards Generalizable Classification of Human Morals and Values (2025.emnlp-main)
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Ziyu Chen, Junfei Sun, Chenxi Li, Tuan Dung Nguyen, Jing Yao, Xiaoyuan Yi, Xing Xie, Chenhao Tan, Lexing Xie
| Challenge: | Identifying human morals and values embedded in language is essential to empirical studies of communication. |
| Approach: | They propose a framework for generalizable classification of human morals and values . they recommend a classification strategy that scores all related concepts simultaneously . |
| Outcome: | The proposed method outperforms fine-tuned models across domains and frameworks. |
Visual Contextual Attack: Jailbreaking MLLMs with Image-Driven Context Injection (2025.emnlp-main)
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| Challenge: | Recent studies have shown that visual encoders can induce harmful behavior in multimodal large language models. |
| Approach: | They propose a vision-centric jailbreak attack that uses visual information to create a jailbreak context. |
| Outcome: | The proposed attack outperforms baseline attacks on MM-SafetyBench and GPT-4o. |
QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)
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| Challenge: | Existing methods to recommend quotes are evaluated on unpublished datasets . |
| Approach: | They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese. |
| Outcome: | The proposed model outperforms existing methods on all three parts of QuoteR. |
Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)
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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang
| Challenge: | Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly. |
| Approach: | They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size . |
| Outcome: | The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly. |
X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from Jailbreak Attacks without Compromising Usability (2025.findings-emnlp)
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| Challenge: | Existing methods for enhancing LLM security compromise usability, study finds . boundary-safe representations close to harmful representations are disrupted, resulting in usability decline . |
| Approach: | They propose a method to push harmful representations away from boundary-safe representations and obtain an exact distinction boundary. |
| Outcome: | The proposed method reduces over-refusal rate and maintains general capability . it pushes harmful representations away from boundary-safe representations, thereby reducing usability. |
Influence-based Online Experience Selection for Effective RLHF (2026.acl-long)
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| Challenge: | Existing methods for RL fail to establish an interpretable connection between data and optimization objectives. |
| Approach: | They propose a data selection method that dynamically estimates the influence of individual training samples on policy optimization. |
| Outcome: | The proposed method significantly improves training effectiveness with fewer optimization steps. |
HistLens: Mapping Idea Change across Concepts and Corpora (2026.acl-long)
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| Challenge: | Existing approaches to diachronic semantics and discourse analysis focus on a single concept or corpus, argues a new paper. |
| Approach: | They propose a framework for multi-concept, multi-corpus conceptual-history analysis that decomposes concept representations into interpretable features and tracks activation dynamics over time and across sources. |
| Outcome: | The proposed framework decomposes concept representations into interpretable features and tracks their activation dynamics over time and across sources. |
Hyperbolic Relevance Matching for Neural Keyphrase Extraction (2022.naacl-main)
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| Challenge: | Keyphrase extraction is a fundamental task in natural language processing that aims to extract a set of phrases with important information from a source document. |
| Approach: | They propose a hyperbolic matching model to explore keyphrase extraction in hyperbolical space using word embeddings from RoBERTa to capture hierarchical syntactic and semantic structures. |
| Outcome: | The proposed model outperforms the state-of-the-art models on six benchmark datasets and outperformed previous models. |
A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models (2023.findings-eacl)
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| Challenge: | Keyphrase extraction is a key component in Natural Language Processing (NLP) systems for selecting a set of phrases from the document that could summarize the important information discussed in the source document. |
| Approach: | They propose to use supervised and unsupervised keyphrase extraction techniques to investigate the state-of-the-art models for keyphrase extracting. |
| Outcome: | The proposed keyphrase extraction system can significantly accelerate the speed of retrieval and help people get first-hand information from a long document quickly and accurately. |
HVGuard: Utilizing Multimodal Large Language Models for Hateful Video Detection (2025.emnlp-main)
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Yiheng Jing, Mingming Zhang, Yong Zhuang, Jiacheng Guo, Juan Wang, Xiaoyang Xu, Wenzhe Yi, Keyan Guo, Hongxin Hu
| Challenge: | Existing methods for hateful video detection rely on unimodal analysis or feature fusion . Existing tools struggle to capture cross-modal interactions and reason through implicit hate in sarcasm and metaphor . |
| Approach: | They propose a reasoning-based hateful video detection framework with multimodal large language models . they integrate Chain-of-Thought reasoning to enhance multimodal interaction modeling . |
| Outcome: | The proposed framework outperforms existing tools on two public datasets covering English and Chinese. |
Improving Embedding-based Unsupervised Keyphrase Extraction by Incorporating Structural Information (2023.findings-acl)
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| Challenge: | Existing unsupervised keyphrase extraction models ignore the indicative role of the highlights in certain locations, leading to wrong keyphrases extraction. |
| Approach: | They propose a Highlight-Guided Unsupervised Keyphrase Extraction model that models phrase-document relevance via the highlights of documents and calculates cross-phrase relevance between all candidate phrases. |
| Outcome: | The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction models on three benchmarks. |
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design (2025.findings-naacl)
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| Challenge: | Text-Attributed Graphs (TAGs) are a powerful tool for understanding complex systems and relationships. |
| Approach: | They propose a benchmark to evaluate large language models for graph-structured data using prompts. |
| Outcome: | The proposed benchmark outperforms state-of-the-art graph LLMs and graph neural networks on graph learning tasks without training. |
Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning (2022.findings-emnlp)
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| Challenge: | Existing delta tuning algorithms freeze most of the parameters and only optimize minimal adaptive parameters. |
| Approach: | They propose to decompose DETs into a unified optimization subspace and conduct optimization within the subspace. |
| Outcome: | The proposed DETs achieve comparable performance to the original DET and can be transferred to another DET with non-trivial performance. |
Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)
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| Challenge: | Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness . |
| Approach: | They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations . |
| Outcome: | The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples . |
Beyond the Panorama: Training-Free Hierarchical Perception-Reasoning for Fine-Grained Vision in MLLMs (2026.acl-long)
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| Challenge: | Existing multimodal large language models (MLLMs) face challenges in fine-grained visual tasks. |
| Approach: | They propose a training-free hierarchical perception-reasoning framework that enhances fine-grained visual understanding by simulating human perception mechanisms. |
| Outcome: | The proposed framework enhances fine-grained visual understanding by simulating human perception mechanisms. |
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference (2022.findings-acl)
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Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, Donald Metzler
| Challenge: | State-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. |
| Approach: | They propose to fine tune a pretrained encoder-decoder model using document to query generation. |
| Outcome: | The proposed model achieves comparable results to more expensive approaches while being 6.8X faster. |
Towards Better Value Principles for Large Language Model Alignment: A Systematic Evaluation and Enhancement (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) show remarkable performance across tasks . alignment with human values is critical for their responsible development. |
| Approach: | They propose a framework that evaluates value principles along three desirable properties . they propose supervised fine-tuning, reinforcement learning-based approaches . |
| Outcome: | The proposed framework improves value principles along the three desirable properties of LLMs. |