Papers by Yi Dong
Disentangled Multi-span Evolutionary Network against Temporal Knowledge Graph Reasoning (2025.findings-acl)
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| Challenge: | Existing methods for temporal knowledge Graphs neglect internal structural interactions between subgraphs and ignore potential smooth features that do not lead to semantic changes. |
| Approach: | They propose to use a disentangled multi-span evolutionary network to capture local neighbor features while perceiving historical neighbor semantic information. |
| Outcome: | Extensive experiments show that the proposed model outperforms the state-of-the-art in TKG reasoning by 22.7%. |
SDBench: A Survey-based Domain-specific LLM Benchmarking and Optimization Framework (2025.acl-long)
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| Challenge: | acquiring domain-specific knowledge often requires professional expert manpower. |
| Approach: | They propose a generic framework for generating evaluation datasets for domain-specific LLMs. |
| Outcome: | The proposed framework reduces the reliance on expert manpower while ensuring that the collected data is uniformly distributed. |
Distributed LLM Serving on Consumer-Grade GPUs by Reconciling Computation and Communication (2025.findings-emnlp)
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Lewei Jin, Kui Zhang, Yongqi Chen, null Zhuoyifan, Renjie Li, Yi Gao, Bowei Yang, Zhengong Cai, Wei Dong
| Challenge: | Large language models are reshaping internet services, and serving them is costly. |
| Approach: | They propose an efficient distributed LLM serving system that splits prefill and decode requests into smaller chunks . |
| Outcome: | The proposed system reduces TTFT, TPOT, and latency compared to the state-of-the-art system. |
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks. |
| Approach: | They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes. |
| Outcome: | The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset. |
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (P18-1)
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| Challenge: | Existing models for matching dialogue responses rely on semantic and functional dependencies . a recent study only uses the last utterance in context for matching a reply . |
| Approach: | They propose a model that matches a response with its multi-turn context using attention. |
| Outcome: | The proposed model outperforms the state-of-the-art models on two large-scale multi-turn response selection tasks. |
HelpSteer3: Human-Annotated Feedback and Edit Data to Empower Inference-Time Scaling in Open-Ended General-Domain Tasks (2025.acl-long)
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Zhilin Wang, Jiaqi Zeng, Olivier Delalleau, Daniel Egert, Ellie Evans, Hoo-Chang Shin, Felipe Soares, Yi Dong, Oleksii Kuchaiev
| Challenge: | Inference-Time Scaling is critical to the success of recent models such as OpenAI o1 and DeepSeek R1 . however, many techniques require tasks to have answers that can be verified . |
| Approach: | They use data to train dedicated Feedback and Edit Models capable of inference-time scaling for open-ended tasks. |
| Outcome: | The proposed model can reach SoTA performance on Arena Hard at 92.7 as of 5 Mar 2025. |
Offline Reinforcement Learning for LLM Multi-step Reasoning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly applied to complex tasks requiring multi-step reasoning. |
| Approach: | They propose an offline method for enhancing multi-step reasoning by optimizing the soft Bellman Equation by combining a policy model and a value function. |
| Outcome: | The proposed method surpasses existing methods on multi-step reasoning benchmarks and can be extended to multi-iteration frameworks when additional resources are available. |
Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026.acl-long)
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| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has been effective on structured tasks, but its reliance on simple, rule-based verifiers creates a bottleneck. |
| Approach: | They propose a framework that uses a generative verifier to provide soft, probabilistic rewards. |
| Outcome: | The proposed framework outperforms existing models up to 10x their size and can be scalable and effective. |
Text Grafting: Near-Distribution Weak Supervision for Minority Classes in Text Classification (2024.emnlp-main)
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| Challenge: | Recent work generates pseudo labels by mining texts similar to the class names from the raw corpus, but there is a high risk that LLMs cannot generate in-distribution data, leading to ungeneralizable classifiers. |
| Approach: | They propose to use LLMs to generate pseudo labels by mining masked templates from corpus . they then use state-of-the-art LLM to synthesize near-distribution texts falling into minority classes . |
| Outcome: | The proposed framework improves on the previous methods for extremely weak-supervised text classification. |
Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage (2026.acl-long)
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| 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. |
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling (2022.coling-1)
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Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu
| Challenge: | Existing slot filling models memorize inherent patterns of entities and contexts from training data. |
| Approach: | They propose a perturbed semantic structure awareness transferring method for slot filling models . they use two MLM-based training strategies to learn contextual semantic structure and word distribution . |
| Outcome: | The proposed method outperforms existing methods and gains strong generalization while preventing model from memorizing inherent patterns of entities and contexts. |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
SocialEval: Evaluating Social Intelligence of Large Language Models (2025.acl-long)
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Jinfeng Zhou, Yuxuan Chen, Yihan Shi, Xuanming Zhang, Leqi Lei, Yi Feng, Zexuan Xiong, Miao Yan, Xunzhi Wang, Yaru Cao, Jianing Yin, Shuai Wang, Quanyu Dai, Zhenhua Dong, Hongning Wang, Minlie Huang
| Challenge: | Existing work on LLMs does not address their social intelligence (SI) and their discrepancy with humans. |
| Approach: | They propose a script-based bilingual SI benchmark that integrates outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation by manually crafting narrative scripts. |
| Outcome: | The proposed model is based on a script-based bilingual evaluation paradigm that integrates outcome- and process-oriented evaluation by manually crafting narrative scripts. |
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)
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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. |
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026.acl-long)
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Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong
| Challenge: | Existing agentic systems are retrieval-heavy but reasoning-light . current systems lack compositional reasoning, a key component of deep research . |
| Approach: | They propose a data synthesis pipeline WebAggregator to shift agentic paradigm . they use Proactive Explorer to collect interconnected knowledge and Compositional Logic Proposer to weave knowledge into complex questions . |
| Outcome: | The proposed pipeline surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench. |
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification (2020.coling-main)
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| Challenge: | Existing methods for generating textual-based explanations are highly implausible and damage a user’s trust in the automated system. |
| Approach: | They propose a method which first applies robust transformer models on a real-world, up-to-date, self-collected mergers and acquisitions dataset and then generates plausible, post-hoc, counterfactual explanations. |
| Outcome: | The proposed model improves model accuracy and human performance while generating plausible explanations based on human trials. |
DeepGen: Diverse Search Ad Generation and Real-Time Customization (2022.emnlp-demos)
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| Challenge: | Existing systems that generate ads manually are not effective in generating ad copy and generating millions of ads for large businesses. |
| Approach: | They propose a system that generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed. |
| Outcome: | The proposed system generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed. |
PUPPET: Neural-Symbolic Standardized Patients for Mental Health (2026.acl-long)
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Chen Xu, Yu ji, Zhenyu Lv, Yang Yi, Yizhe Yang, Luyao Ji, Chaoyi Chen, Xianyang Wang, Tian Lan, Zhihua Wang, Juan Wang, Xunde Dong, Fuze Tian, Qunxi Dong, Bin Hu
| Challenge: | Existing LLM-based training approaches lack faithful responses to clinical errors and explainable feedback. |
| Approach: | They propose a neural-symbolic virtual standardized patient governed by an OBSERVE-THINK-BEHAVE architecture that embeds LLM reasoning into a symbolic system where experts implant causal associations between intervention logic and patient mental states. |
| Outcome: | The proposed model outperforms baselines in faithfulness and pedagogical value. |
Athena: Safe Autonomous Agents with Verbal Contrastive Learning (2024.emnlp-industry)
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| Challenge: | Existing safety benchmarks on the ability of large language models to perform tasks are lacking. |
| Approach: | They propose a framework that leverages verbal contrastive learning to guide agents towards safety . they use past safe and unsafe trajectories as in-context examples to guide them towards safety. |
| Outcome: | The proposed framework leverages verbal contrastive learning to guide agents towards safety while performing tasks. |
MC-indexing: Effective Long Document Retrieval via Multi-view Content-aware Indexing (2024.findings-emnlp)
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| Challenge: | Existing methods for document question answering do not consider content structures, resulting chunks exclude vital information or include irrelevant content. |
| Approach: | They propose a method that segments document into content chunks and represents each content chunk in raw-text, keywords, and summary views. |
| Outcome: | The proposed method significantly improves recall of long document question answering datasets compared to state-of-the-art chunking schemes. |
HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM (2024.naacl-long)
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Zhilin Wang, Yi Dong, Jiaqi Zeng, Virginia Adams, Makesh Narsimhan Sreedhar, Daniel Egert, Olivier Delalleau, Jane Scowcroft, Neel Kant, Aidan Swope, Oleksii Kuchaiev
| Challenge: | Existing helpfulness preference datasets do not specify what makes some responses more helpful and others less helpful. |
| Approach: | They use a dataset that has annotated for correctness, coherence, complexity, and verbosity. |
| Outcome: | The dataset has annotations for correctness, coherence, complexity, and verbosity in addition to overall helpfulness of responses. |
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study (2023.emnlp-main)
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Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro
| Challenge: | a recent study shows that retrieval-augmented LMs can improve text generation quality and accuracy. |
| Approach: | They propose a model that reproduces RETRO parameters while retrieving a text corpus . they find RETRO outperforms GPT on text generation with less repetition . |
| Outcome: | The proposed model outperforms standard retrieval-augmented GPT and retrieval augmented GTP on text generation and accuracy tasks. |
VillagerAgent: A Graph-Based Multi-Agent Framework for Coordinating Complex Task Dependencies in Minecraft (2024.findings-acl)
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| Challenge: | Multi-agent collaboration using LLMs is a challenging research topic that aims to enable multiple autonomous agents to coordinate their actions and achieve a common goal. |
| Approach: | They propose a benchmark for multi-agent collaboration in the Minecraft environment and introduce a Directed Acyclic Graph Multi-Agent Framework to resolve complex inter-ag dependencies. |
| Outcome: | The proposed framework outperforms existing ModelVerse, reducing hallucinations and improving task decomposition efficacy. |
CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)
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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions. |
| Approach: | They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges. |
| Outcome: | Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4. |
TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models (2025.acl-industry)
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| Challenge: | Tabular data analysis is crucial in many scenarios, yet its complexity and density can make it challenging to determine the most appropriate analysis operations for a new table. |
| Approach: | They propose a tabular data analysis framework that recommends query-code-result triplets for new tables . they propose Rec-Align, a method to further improve recommendation quality . |
| Outcome: | The proposed framework achieves 77.0% top-5 recommendation recall on a dataset designed for tabular data analysis recommendation. |
IoTMigrator: LLM-driven Embedded IoT Code Migration across Different OSes for Cloud-device Integration (2025.findings-emnlp)
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| Challenge: | Neither outline-based code generation nor common code translation techniques can adequately address this challenge, despite their prevalence in existing systems. |
| Approach: | They have developed an algorithm that employs a multi-agent pipeline to handle embedded code migration under the TSL paradigm. |
| Outcome: | The proposed algorithm outperforms the baseline by 50.5% for pass rate and 13.0% for completeness across all tasks in RIOT and Zephyr. |
Chain-of-Thought as a Lens: Evaluating Structured Reasoning Alignment between Human Preferences and Large Language Models (2026.acl-long)
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| Challenge: | Empirical results show misalignment at greater reasoning depths is driven mainly by alignment errors such as thematic shift and redundant reasoning. |
| Approach: | They propose a method to quantitatively assess the alignment between multi-step, structured reasoning in large language models and human preferences by constructing semantic-entropy-based matrices over intermediate steps and measuring their divergence. |
| Outcome: | The proposed method shows that it is consistent with previous studies and can be used as a diagnostic signal. |
SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF (2023.findings-emnlp)
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| Challenge: | supervised fine-tuning and reinforcement learning from human feedback (RLHF) are not effective in generating useful and high-quality responses. |
| Approach: | They propose a supervised fine-tuning method that empowers end-users to control responses during inference. |
| Outcome: | Experiments show that supervised fine-tuning and reinforcement learning from human feedback (RLHF) can generate helpful and high-quality responses while maintaining customizability. |
Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training (2026.acl-long)
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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. |
CLGC: A Corpus for Chinese Literary Grace Evaluation (2022.lrec-1)
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| Challenge: | Literature grace is a key element of the style and quality of articles in China. |
| Approach: | They propose to annotate a Chinese literary grace corpus with 10,000 texts and 1.85 million tokens and build a literary grace evaluation task to assess the literary grace level. |
| Outcome: | The proposed model achieves 79.71% on the weighted average F1-score. |
Prosody-TTS: Improving Prosody with Masked Autoencoder and Conditional Diffusion Model For Expressive Text-to-Speech (2023.findings-acl)
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| Challenge: | Expressive text-to-speech aims to generate high-quality samples with rich prosody . prosodic attributes in highly dynamic voices are difficult to capture and model without intonation . |
| Approach: | They propose a pipeline that enhances prosody modeling and sampling by introducing a self-supervised masked autoencoder and a diffusion model to sample diverse prosodic patterns within the latent space. |
| Outcome: | The proposed pipeline achieves new state-of-the-art in text-to-speech with natural and expressive synthesis. |