Papers by Yi Sun
Effective and Efficient Query-aware Snippet Extraction for Web Search (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to extract webpage snippets ignore contextual information of webpages, which may be sub-optimal. |
| Approach: | They propose a query-aware webpage snippet extraction method called DeepQSE that captures contextual information of webpages. |
| Outcome: | The proposed method can significantly improve the performance of DeepQSE without affecting its performance. |
Parallel Data Augmentation for Formality Style Transfer (2020.acl-main)
Copied to clipboard
| Challenge: | Formality style transfer is a task of automatically transforming text in one particular formality style into another. |
| Approach: | They propose to augment parallel data with three specific data augmentation methods to improve the model's generalization ability and reduce the overfitting risk. |
| Outcome: | The proposed methods significantly improve performance when used to pre-train the model and lead to the state-of-the-art results in the GYAFC benchmark dataset. |
ATLANTIS: Weak-to-Strong Learning via Importance Sampling (2025.acl-long)
Copied to clipboard
| Challenge: | ATLANTIS is a new technique to improve the performance of large language models. |
| Approach: | They propose a new technique to bridge the gap between the distribution of current datasets and the real-world data distribution by using importance sampling. |
| Outcome: | The proposed technique can bring consistent and significant improvements to models’ performance and can be flexibly transferred among models with different structures. |
WhitenedCSE: Whitening-based Contrastive Learning of Sentence Embeddings (2023.acl-long)
Copied to clipboard
| Challenge: | Extensive experiments on seven semantic textual similarity tasks show our method achieves consistent improvement over the contrastive learning baseline and sets new states of the art. |
| Approach: | They propose a whitening-based contrastive learning method for sentence embedding learning which combines contrastive and shuffled group whitening. |
| Outcome: | The proposed method achieves better alignment and uniformity on seven semantic textual similarity tasks. |
FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner (2026.acl-long)
Copied to clipboard
Hao Wu, Hongru Sun, Wanqing Li, Xinguo Yu, Hao Ming, Xiao Luo, Wenbin Zhang, Jiahong Zhao, Yi Guo, Jie Yang
| Challenge: | Existing approaches to mathematical reasoning rely on static heuristics or pre-determined reasoning strategies. |
| Approach: | They propose an adaptive framework that integrates fuzzy theory into LLM-based mathematical reasoning. |
| Outcome: | The proposed framework outperforms state-of-the-art models while offering effective and interpretable diagnostics of intermediate problem-solving states. |
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation (D18-1)
Copied to clipboard
| Challenge: | Existing models for narrative story generation lack semantic dependency among sentences. |
| Approach: | They propose a skeleton-based model that generates the most critical phrases and expands them to a complete sentence. |
| Outcome: | The proposed model can generate significantly more coherent stories according to human evaluation and automatic evaluation. |
Robust Representation Learning of Biomedical Names (P19-1)
Copied to clipboard
| Challenge: | Biomedical concepts are often mentioned in medical documents under different name variations. |
| Approach: | They propose a framework for learning robust representations of biomedical names and terms . they encode contextual meaning, conceptual meaning, and similarity between synonyms . |
| Outcome: | The proposed framework outperforms baselines on retrieval, similarity and relatedness benchmarks. |
Learning Sentiment Memories for Sentiment Modification without Parallel Data (D18-1)
Copied to clipboard
| Challenge: | Existing methods for sentiment modification generate input-irrelevant texts due to lack of parallel data. |
| Approach: | They propose a method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. |
| Outcome: | The proposed method significantly improves the content preservation degree and achieves the state-of-the-art performance. |
DynaMaR: Dynamic Prompt with Mask Token Representation (2022.emnlp-industry)
Copied to clipboard
Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam, Weiyi Lu, Yi Xu, Iman Keivanloo, Belinda Zeng, Trishul Chilimbi
| Challenge: | Recent research shows that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. |
| Approach: | They propose an unsupervised approach to fine-tuning large language models using unsupervised approaches to many downstream tasks. |
| Outcome: | The proposed approach improves on four e-commerce applications and can achieve an average improvement of 10% in few-shot settings and 3.7% in data-rich settings over the standard approach. |
Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
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. |
On the Vulnerability of Safety Alignment in Open-Access LLMs (2024.findings-acl)
Copied to clipboard
Jingwei Yi, Rui Ye, Qisi Chen, Bin Zhu, Siheng Chen, Defu Lian, Guangzhong Sun, Xing Xie, Fangzhao Wu
| Challenge: | Large language models (LLMs) are susceptible to malicious exploitation, but are often rejected and limited harmfulness is limited. |
| Approach: | They propose two types of reverse alignment techniques: reverse supervised fine-tuning (RSFT) and reverse preference optimization (RPO). |
| Outcome: | The proposed methods can significantly enhance the success rate and harmfulness of jailbreak attacks, but they face high rejection rates and limited harmfulness. |
FPE2M2: Approaching Lossless and Efficient Quantization with Native Floating Point (2025.findings-acl)
Copied to clipboard
Ke Yi, Jianwei Zhang, Zhiying Xu, Xinlong Yang, Yang Zhou, Minmin Sun, Zengke Liu, Tong Zhang, Junyang Lin, Jingren Zhou
| Challenge: | Auto-regressive decoding is a memory-bound job, meaning decoding performance is limited by the bandwidth rather than the computational capabilities of the GPU. |
| Approach: | They propose a framework that supports lossless weight-only quantization inference and validate it on Qwen and LLaMA Models. |
| Outcome: | The proposed framework achieves the highest efficiency with lossless accuracy on Qwen and LLaMA Models across various modalities. |
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement (2025.coling-main)
Copied to clipboard
Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi Fung, Hou Pong Chan, Kevin Small, ChengXiang Zhai, Heng Ji
| Challenge: | Existing research has focused on enhancing the retrieval stage and optimizing the representation of the database. |
| Approach: | They propose a framework to improve generalization across task contexts and collaborative refinement to bridge knowledge gaps among users. |
| Outcome: | The proposed framework improves generalization across task contexts and collaborative refinement to bridge knowledge gaps among users. |
CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models (2026.acl-long)
Copied to clipboard
Haibo Tong, Zeyang Yue, Feifei Zhao, Erliang Lin, Lu Jia, Ruolin Chen, Yinqian Sun, Qian Zhang, Yi Zeng
| Challenge: | Existing benchmarks for Large Language Models (LLMs) are limited to false belief tasks, highlighting bottlenecks in specific dimensions. |
| Approach: | They propose a benchmark to evaluate Large Language Models' Theory of Mind capabilities . they evaluate 8000 bilingual instances across 46 paradigms and validated by 49 human annotators . |
| Outcome: | The proposed benchmark reveals performance heterogeneities and bottlenecks in 22 representative models. |
Automatic Poetry Generation with Mutual Reinforcement Learning (D18-1)
Copied to clipboard
| Challenge: | Existing models for automatic poetry generation are based on maximum likelihood estimation (MLE) MLE-based models tend to remember common patterns of the poetry corpus, which results in loss-evaluation mismatch. |
| Approach: | They propose to model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning to motivate the model to pursue higher scores. |
| Outcome: | The proposed model outperforms the current state-of-the-art model and improves on Chinese poetry. |
Towards Database-Free Text-to-SQL Evaluation: A Graph-Based Metric for Functional Correctness (2025.coling-main)
Copied to clipboard
Yi Zhan, Longjie Cui, Han Weng, Guifeng Wang, Yu Tian, Boyi Liu, Yingxiang Yang, Xiaoming Yin, Jiajun Xie, Yang Sun
| Challenge: | Existing metrics for evaluating functional correctness of SQL queries are prone to false positives due to inadequately prepared test databases. |
| Approach: | They propose a graph-based metric that uses a relational operator tree to extract rich semantic information from the logical execution plan of SQL queries and embed it into a diagram. |
| Outcome: | The proposed method eliminates the need for extensive test database preparation and performs graph matching on unseen SQL queries. |
PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension (2025.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks on punchline comprehension suffer from language shortcuts that allow models to rely on text, lack of question diversity, and narrow focus on a specific domain of multimodal content. |
| Approach: | They propose a multimodal punchline comprehension benchmark to assess models' ability to comprehend punchlines. |
| Outcome: | The proposed model surpasses in-context learning and chain-of-thought in punchline comprehension. |
Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be prohibitive in terms of feasibility, time, and resources. |
| Approach: | They propose a method for training large language models by enabling "self-talk" they propose supervised fine-tuning of LLMs to improve quality of dialogues . |
| Outcome: | The proposed method generates training data via "self-talk" of LLMs that can be refined and utilized for supervised fine-tuning. |
Multimodal Sarcasm Target Identification in Tweets (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to detect sarcasm target with text lacking context are not sufficient and complete. |
| Approach: | They propose a multi-modal sarcasm target identification task that performs both textual and visual detection. |
| Outcome: | The proposed model can perform textual target labeling and visual target detection. |
OccuTriage: An AI Agent Orchestration Framework for Occupational Health Triage Prediction (2025.acl-industry)
Copied to clipboard
| Challenge: | Experimental evaluation on 2,589 occupational health cases demonstrates that OccuTriage outperforms single-agent approaches with a 20.16% average discordance rate compared to baseline rates of 43.05% . |
| Approach: | They propose to use specialized LLM agents, retrieval augmentation, and a bidirectional decision architecture to simulate healthcare professionals’ reasoning. |
| Outcome: | The proposed framework outperforms single-agent approaches with a 20.16% average discordance rate while matching or exceeding human expert performance. |
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that powerful Transformer architectures produce dull high-frequency phrases, severely hurting the diversity and novelty of generated text. |
| Approach: | They propose a method to control the sharpness of the attention distribution by python code and use it to learn a Bayesian approximation of posterior attention. |
| Outcome: | The proposed method improves diversity and novelty while maintaining comparable quality on conditional and unconditional generation tasks. |
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization (2024.naacl-long)
Copied to clipboard
Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown
| Challenge: | Existing LLMs hallucinate significant amounts of factual errors in the dialogue domain, regardless of the model’s size. |
| Approach: | They propose to evaluate topic-focused dialogue summarization by using large language models (LLMs) they use human annotations to evaluate factual consistency and explain factually inconsistent sentences. |
| Outcome: | The proposed evaluation benchmark on topic-focused dialogue summarization shows that existing LLMs hallucinate significant amounts of factual errors regardless of the model’s size. |
Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) evolve to autonomous agents synthesizing real-time information, but their reasoning capabilities introduce an unexpected attack surface. |
| Approach: | They propose a framework that constructs deceptive narratives through adversarial debate and coordinated posting of evidence fragments, causing victims to internalize and propagate fabricated conclusions. |
| Outcome: | The proposed framework constructs deceptive narratives through adversarial debate and coordinated posting of evidence fragments, causing victims to internalize and propagate fabricated conclusions. |
PRDetect: Perturbation-Robust LLM-generated Text Detection Based on Syntax Tree (2025.findings-naacl)
Copied to clipboard
| 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. |
AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction (2026.findings-acl)
Copied to clipboard
Pollawat Hongwimol, Haoning Shang, Chutong Wang, Zhichao Wan, Yi Gao, Yuanming Li, Lin Gui, Wenhao Sun, Cheng Yu
| Challenge: | Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain. |
| Approach: | They propose a multi-agent Large Language Model framework that constructs a Product-attribute Knowledge Graph from multimodal product content. |
| Outcome: | The proposed framework achieves 0.953 WKE for product types, 0.724 WKEs for attribute keys, and 0.531 edge-level accuracy for value assertions after canonicalization on a large real-world marketplace catalog dataset from Lazada (Alibaba). |
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark (2023.acl-long)
Copied to clipboard
Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, Xing Xie
| Challenge: | Large language models (LLMs) have demonstrated exceptional abilities in both text understanding and generation. |
| Approach: | They propose an Embedding Watermark method that implants backdoors on embeddings to protect copyright of large language models. |
| Outcome: | The proposed method protects the copyright of large language models without compromising service quality while minimizing the adverse impact on the original embeddings’ utility. |
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)
Copied to clipboard
Han Weng, Puzhen Wu, Cui Longjie, Yi Zhan, Boyi Liu, Yuanfeng Song, Dun Zeng, Yingxiang Yang, Qianru Zhang, Dong Huang, Xiaoming Yin, Yang Sun, Xing Chen
| Challenge: | Existing methods to enhance performance of large language models (LLMs) on Text-to-SQL tasks rely on execution-based or LLM-based reward models. |
| Approach: | They propose a reward model framework for RL-based Text-to-SQL that employs the GMNScore outcome reward model. |
| Outcome: | The proposed reward model outperforms existing reward models on standard benchmarks including Spider and BIRD. |
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing frameworks for federated multilingual neural machine translation (Fed-MNMT) are limited in language resources. |
| Approach: | They propose a framework that keeps PLMs frozen and only transfers lightweight adapter modules between clients. |
| Outcome: | The proposed framework reduces communication cost by over 98% while achieving similar or even better performance compared to baselines. |
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to accelerate inference speed of pre-trained language models are limited to local representations of exit layer . current models are associated with large memory requirement and high computational cost, which slow down inference and further encumber the application of PLMs. |
| Approach: | They propose a method to exit early without passing through all inference layers . they take into consideration all the linguistic information embedded in the past layers a global perspective . |
| Outcome: | The proposed method outperforms existing methods by a large margin . it uses linguistic information embedded in the past layers and future features . the proposed method is scalable and cost-effective . |
Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation (2025.acl-demo)
Copied to clipboard
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. |
Feedback to Reasoning: LLM-Assisted Molecular Optimization with Domain Feedback and Historical Reasoning (2026.findings-acl)
Copied to clipboard
Wenhan Gao, Xiran Fan, Chin-Chia Michael Yeh, Jiarui Sun, Yuzhong Chen, Menghai Pan, Mahashweta Das, Yi Liu
| Challenge: | Existing methods for molecular optimization do not leverage domain feedback and historical knowledge with reasoning traces and chemical insights. |
| Approach: | They propose a conversational molecular optimization pipeline that enables LLMs to accumulate and retrieve past actions, rationales, and feedback. |
| Outcome: | The proposed framework transforms LLMs from passive text generators into agentic experts that learn both actions and reasoning from experience. |
Punctuation-Steered Representation Fine-Tuning (2026.acl-short)
Copied to clipboard
| Challenge: | Existing methods for parameter-efficient fine-tuning (PeFT) are limited due to their prohibitive size and computational demands. |
| Approach: | They propose a method that fine-tunes punctuation representations to achieve performance improvements. |
| Outcome: | The proposed method improves performance by altering the representation space alone . but it results in suboptimal performance due to the effects of the method on the output . |
TempCompass: Do Video LLMs Really Understand Videos? (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks on video large language models lack a comprehensive feedback on temporal perception ability . current models cannot distinguish between different temporal aspects and are limited in task formats . |
| Approach: | They propose a benchmark to evaluate temporal perception ability of video large language models . they construct conflicting videos that share the same static content but differ in a specific temporal aspect . |
| Outcome: | The proposed benchmarks show that video large language models exhibit poor temporal perception ability. |
FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)
Copied to clipboard
Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo
| Challenge: | Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment. |
| Approach: | They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling . |
| Outcome: | The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines. |
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? (2023.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts. |
| Approach: | They analyze a visual question answering dataset tailored for info-seeking questions . they show that pre-trained visual and language models can use fine-grained knowledge . |
| Outcome: | The proposed dataset elicits models to use fine-grained knowledge learned during pre-training. |
MoVa: Towards Generalizable Classification of Human Morals and Values (2025.emnlp-main)
Copied to clipboard
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. |
EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions (2026.findings-acl)
Copied to clipboard
| Challenge: | Multimodal Large Language Models (MLLMs) are a promising tool for traditional education but lack authentic and domain-specific benchmarks to accurately interpret student handwritten solutions. |
| Approach: | They propose to use MLLMs to interpret unconstrained STEM student handwritten solutions with intertwined mathematical formulas, diagrams, and textual reasoning to bridge this gap. |
| Outcome: | The proposed model can detect and rectify recognition errors with minimal human intervention on unseen student solutions. |
QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)
Copied to clipboard
| 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. |
PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption (2026.findings-acl)
Copied to clipboard
Xue Tan, Yi Zheng, Chang Huo, Yunruo Zhang, Yu Liu, Hao Luan, Zhuyang Yu, Jun Dai, Xiaoyan Sun, Ping Chen
| Challenge: | Existing defense mechanisms lack theoretical robustness guarantees and perform unreliably when the LLM has limited knowledge of the retrieved content. |
| Approach: | They propose a provably robust retrieval aggregation algorithm designed to defend against poisoning attacks on retrieved texts. |
| Outcome: | Experiments show that PRA-RAG reduces the attack success rate to as low as 1% while maintaining an accuracy of 71%, significantly outperforming representative state-of-the-art (SOTA) methods. |
Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization (2026.acl-long)
Copied to clipboard
Shaohua Duan, Pengcheng Huang, Xinze Li, Zhenghao Liu, Xiaoyuan Yi, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu, Maosong Sun
| Challenge: | Recent studies have explored fine-tuning Large Language Models with synthetic data to enhance their long-context capabilities. |
| Approach: | They propose a framework that leverages a Multi-Armed Bandit rollout strategy to identify the most informative chunks from the given long context for sampling high-quality and diverse responses. |
| Outcome: | The proposed framework achieves 4% improvement on long-context reasoning benchmarks on Llama and Qwen. |
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing news recommendation methods rely on centralized storage of user click behavior data, which may lead to privacy concerns and hazards. |
| Approach: | They propose a federated learning framework for privacy-preserving news recommendation . they propose aggregation of news representations and user model by a client . |
| Outcome: | The proposed framework reduces computation and communication cost on clients while keeping promising model performance. |
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations. |
| Approach: | They propose to use pretrained language models to enhance knowledge representation by leveraging world knowledge from pretrained models. |
| Outcome: | Extensive experiments show that the proposed framework can improve results over existing models. |
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Variational Auto-Encoder (VAE) has been widely adopted in text generation due to its ability to learn flexible representations. |
| Approach: | They propose a Transformer-based recurrent VAE structure that imposes recurrence on segment-wise latent variables with arbitrarily separated text segments and constructs the posterior distribution with residual parameterization. |
| Outcome: | The proposed structure can deduce a non-zero lower bound of the KL term and enhance the entanglement of each segment and preceding latent variables, providing a theoretical guarantee of generation diversity. |
Gloss-Free End-to-End Sign Language Translation (2023.acl-long)
Copied to clipboard
| Challenge: | a study of sign language translation without gloss annotations focuses on the problem of gloss annotation . gloss annotation is hard to acquire, especially in large quantities, and limits the domain coverage of translation datasets . |
| Approach: | They propose a gloss-free end-to-end sign language translation framework to solve this problem . gloss annotations are hard to acquire, especially in large quantities, they argue . |
| Outcome: | The proposed framework improves sign language translation performance on large-scale datasets . gloss annotations are hard to acquire, especially in large quantities . |
Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement (D18-1)
Copied to clipboard
| Challenge: | Automatic Chinese poetry generation is one of the first attempts towards computer writing. |
| Approach: | They propose a model which requires no supervised style labeling to generate stylistic poems . they incorporate mutual information, a concept in information theory, into modeling . |
| Outcome: | The proposed model generates stylistic poems without losing fluency and coherency . it is based on mutual information, a concept in information theory . |
UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026.acl-long)
Copied to clipboard
Zhen Fang, Ruiyan Han, XinYu Sun, Yuchen Ma, Ziheng Wang, Yu Zeng, Zehui Chen, Lin Chen, Wenxuan Huang, Wei-Jie Xu, Yi Cao, Feng Zhao
| Challenge: | Unified Multimodal Models have achieved remarkable success in cross-modal comprehension, but a gap persists in their ability to translate internal knowledge into faithful and controllable synthesis. |
| Approach: | They propose a self-improvement framework that partitions a single UMM into three collaborative roles: Proposer, Solver, and Judge. |
| Outcome: | The proposed framework improves on TIIF, DPG, CompBench and UniCycle benchmarks. |
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing GEC models produce spurious corrections or fail to detect lots of errors. |
| Approach: | They propose a neural network for GEC quality estimation with multiple hypotheses . VERNet establishes interactions among hypothese based on reasoning graph . |
| Outcome: | The proposed model achieves state-of-the-art grammatical error detection performance and best quality estimation results on four GEC datasets. |
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to forecast news media responses have limited exploration of how to best process and utilize these important features. |
| Approach: | They propose a framework that leverages a large language model to induce a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. |
| Outcome: | The proposed framework surpasses state-of-the-art in experimental evaluations for both zero-shot and supervised settings, demonstrating its effectiveness in response forecasting. |
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Chain-of-thought (CoT) prompting is a technique to enhance the reasoning abilities of Large language models (LLMs) however, the reasoning chains of demonstrations are observed to be prone to errors, which can lead to incorrect reasoning during inference. |
| Approach: | They propose an iterative bootstrapping technique to enhance the reasoning abilities of Large language models (LLMs) by generating a series of reasoning steps to obtain the answer, and using the reasoning chains as exemplars to demonstrate the task. |
| Outcome: | The proposed method improves the performance of Large language models (LLMs) on three reasoning tasks on ten datasets. |
Translation as Cross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-Domain Segmenting and Labeling Tasks (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Chinese has no word delimiter or inflection that can indicate segment boundaries or word semantics, increasing the difficulty of segmenting and labeling tasks. |
| Approach: | They propose a paradigm based on attention augmentation to introduce crucial cross-domain knowledge via a translation system into Chinese model. |
| Outcome: | The proposed model significantly advances the state-of-the-art results of Chinese cross-domain segmenting and labeling tasks. |
A Chinese Dataset with Negative Full Forms for General Abbreviation Prediction (L18-1)
Copied to clipboard
| Challenge: | a common phenomenon across languages is abbreviation, but it's not always possible to predict it accurately. |
| Approach: | They build a dataset for general Chinese abbreviation prediction using a negative full form . they find that abbrevation prediction can improve the performance of abbreviation recognition . |
| Outcome: | The proposed dataset evaluates models on abbreviation prediction in Chinese . it shows that abbrevation prediction improves performance in language processing tasks . |
Using Natural Sentence Prompts for Understanding Biases in Language Models (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent work has shown that language models are susceptible to biases present in the training dataset. |
| Approach: | They propose to use natural sentence prompts to analyze gender-occupation biases in language models. |
| Outcome: | The proposed dataset can be used to analyze gender-occupation biases in language models. |
Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System (P19-3)
Copied to clipboard
Guo Zhipeng, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, Jiannan Liang, Huimin Chen, Yuhui Zhang, Ruoyu Li
| Challenge: | Existing systems for automatic poetry generation are model-oriented, resulting in poor user participation. |
| Approach: | They propose a human-machine collaborative Chinese classical poetry generation system called Jiuge . Jiuge allows users to revise unsatisfied parts of a generated poem draft repeatedly . |
| Outcome: | The proposed system allows users to revise unsatisfied parts of a generated poem draft repeatedly. |
A Critical Analysis of Document Out-of-Distribution Detection (2023.findings-emnlp)
Copied to clipboard
Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova
| Challenge: | Existing document understanding models focus on single-modal inputs such as images or texts. |
| Approach: | They propose to use a spatial-aware adapter to adapt transformer-based language models to document domain to exploit multi-modal information. |
| Outcome: | The proposed model significantly improves the OOD detection performance compared to using a standard language model and to competitive baselines. |
BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Safety backdoors in large language models can be triggered while evading detection during normal interactions. |
| Approach: | They propose a bi-level optimization method that uses a key insight: backdoor triggers induce a uniform drift in the model’s embedding space . inner level identifies universal perturbations to the decoder’s embedded spaces that steer the model towards defender-defined unwanted behaviors; outer level fine-tunes the model to reinforce safe behaviors against these perturbations. |
| Outcome: | The proposed mitigation method reduces the success rate of safety backdoor attacks from over 95% to 1% for general harmful behaviors and from 47% to 0% for Sleeper Agents, without compromising the model’s usefulness. |
Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data? (C18-1)
Copied to clipboard
| Challenge: | Existing neural models take long distance dependencies into account when predicting the tag of the current token. |
| Approach: | They propose a method to capture long distance tag dependencies and use them for dependency analysis. |
| Outcome: | The proposed model can predict multiple tags for the current token without taking dependencies between tags into account. |
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation (2022.naacl-main)
Copied to clipboard
| Challenge: | Variational Auto-Encoders are often used for text generation tasks due to the sequential nature of the text. |
| Approach: | They propose a variational Transformer framework that learns a series of layer-wise latent variables with each inferred from those of lower layers and tightly coupled with the hidden states by low-rank tensor product. |
| Outcome: | The proposed framework can learn latent variables from lower layers and incorporate more information. |
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods to learn incessantly emerging novel relations are overfitting the few memorized examples of old relations, causing confusion among existing relations. |
| Approach: | They introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning. |
| Outcome: | The proposed method outperforms state-of-the-art models in catastrophic forgetting old relations. |
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design (2025.findings-naacl)
Copied to clipboard
| 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. |
Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification (2022.emnlp-industry)
Copied to clipboard
Jai Gupta, Yi Tay, Chaitanya Kamath, Vinh Tran, Donald Metzler, Shailesh Bavadekar, Mimi Sun, Evgeniy Gabrilovich
| Challenge: | a new method for classification of COVID-19 vaccination related search queries is proposed . the proposed method uses pretrained Transformers and dense features to generate search insights . |
| Approach: | They propose a machine learning model that detects COVID-19 vaccination related search queries . they use pretrained Transformers to consider dense features as memory tokens that the model can attend to . |
| Outcome: | The proposed model improves the Vaccine Search Insights task by +15% . the proposed model uses pretrained Transformers and traditional dense features . |
An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint (2025.emnlp-main)
Copied to clipboard
Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu
| Challenge: | Large Language Models (LLMs) are a powerful tool for test-time scaling, but they are often used under time constraints. |
| Approach: | They propose to use LLMs to make models think before answering questions . they also use self-correction and best-of-N decoding to encourage deeper thinking . |
| Outcome: | The proposed models are able to achieve higher inference accuracy with extra inference computation under time constraints. |
Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning (2022.findings-emnlp)
Copied to clipboard
| 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)
Copied to clipboard
| 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 . |
When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Personalization can inadvertently distort factual reasoning when faced with factual queries. |
| Approach: | They propose a lightweight inference-time approach that mitigates personalization-induced factual distortions while preserving personalized behavior. |
| Outcome: | Experiments across multiple LLM backbones and personalization methods show that FPPS significantly improves factual accuracy while maintaining personalized performance. |
Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)
Copied to clipboard
Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations. |
| Approach: | They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference. |
| Outcome: | The proposed method significantly mitigates object hallucinations across various model architectures. |
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks (2022.naacl-industry)
Copied to clipboard
Weiyi Lu, Sunny Rajagopalan, Priyanka Nigam, Jaspreet Singh, Xiaodi Sun, Yi Xu, Belinda Zeng, Trishul Chilimbi
| Challenge: | Multi-task learning (MTL) aims to solve multiple tasks by sharing a base representation among them. |
| Approach: | They propose an approach that allows for "asynchronous" convergence among the tasks where each task can converge on its own schedule. |
| Outcome: | The proposed method outperforms existing methods in two 5-task MTL setups. |
Generative Personality Simulation via Theory-Informed Structured Interview (2026.eacl-long)
Copied to clipboard
Pengda Wang, Huiqi Zou, Han Jiang, Hanjie Chen, Tianjun Sun, Xiaoyuan Yi, Ziang Xiao, Frederick L. Oswald
| Challenge: | Personality structured interviews are often lacking in advancing social science research. |
| Approach: | They propose a method to incorporate psychological insights into LLM simulations . they use a measure theory grounded evaluation procedure to evaluate reliability and validity . |
| Outcome: | The proposed method improves human-like heterogeneity in LLM-simulated personality data and predicts personality-related behavioral outcomes. |
Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training (2026.acl-long)
Copied to clipboard
Changhao Jiang, Ming Zhang, Yifei Cao, Junjie Ye, Xiaoran Fan, Shihan Dou, Zhiheng Xi, Jiajun Sun, Yi Dong, Yujiong Shen, Jingqi Tong, Baoyu Fan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing methods to predict performance of large language models are lacking . authors propose a size-dependent mutual information predictor for closed-book question answering accuracy . |
| Approach: | They propose a size-dependent mutual information predictor that integrates knowledge frequency, knowledge specificity, and model size to forecast closed-book question answering accuracy. |
| Outcome: | The proposed method outperforms baseline models and achieves R2 > 0.7 in predicting QA accuracy without additional training. |
NSP-BERT: A Prompt-based Few-Shot Learner through an Original Pre-training Task —— Next Sentence Prediction (2022.coling-1)
Copied to clipboard
| Challenge: | Recent studies have shown that using prompts to utilize language models to perform downstream tasks is more effective than using token-level methods such as PET. |
| Approach: | They propose to use a BERT original pre-training task abandoned by RoBERTa and other models to construct a sentence-level prompt-based method that does not need to fix the length of the prompt or the position to be predicted. |
| Outcome: | The proposed method performs better than PET and EFL on a BERT pre-training task and is comparable to other prompt-based methods. |
Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models (2024.naacl-long)
Copied to clipboard
Yi Luo, Zhenghao Lin, YuHao Zhang, Jiashuo Sun, Chen Lin, Chengjin Xu, Xiangdong Su, Yelong Shen, Jian Guo, Yeyun Gong
| Challenge: | Guide-Align is a guideline-oriented approach to augment the safety and quality of Large Language Models. |
| Approach: | They propose a guideline-oriented method to augment the safety and quality of large language models. |
| Outcome: | The proposed method outperforms existing methods on three benchmarks and shows significant improvements in security and quality. |