Papers by Tian Gao
A Mutual Information Perspective on Knowledge Graph Embedding (2025.acl-long)
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| Challenge: | Existing knowledge graph embedding techniques suffer from high intra-group similarity, loss of semantic information, and insufficient inference capability, particularly in complex relation patterns such as 1-N and N-1 relations. |
| Approach: | They propose a knowledge graph embedding framework that leverages mutual information maximization to improve the semantic representation of entities and relations. |
| Outcome: | Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method, with consistent performance improvements across various baseline models. |
ReviewRL: Towards Automated Scientific Review with RL (2025.emnlp-main)
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Sihang Zeng, Kai Tian, Kaiyan Zhang, Yuru Wang, Junqi Gao, Runze Liu, Sa Yang, Jingxuan Li, Xinwei Long, Jiaheng Ma, Biqing Qi, Bowen Zhou
| Challenge: | Existing automated review systems struggle with factual accuracy, rating consistency, and analytical depth. |
| Approach: | They propose a framework for generating comprehensive and factually grounded scientific paper reviews using supervised fine-tuning and reinforcement learning. |
| Outcome: | The proposed framework outperforms existing methods on ICLR 2025 papers. |
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis (2020.acl-main)
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| Challenge: | sentiment knowledge is ignored in sentiment analysis, despite its use in pretraining. |
| Approach: | They propose to use sentiment knowledge to learn a unified sentiment representation for multiple sentiment analysis tasks. |
| Outcome: | The proposed method outperforms strong pre-training baseline on three kinds of sentiment tasks. |
Know Your Place: Diagnosing Implicit Social Adaptation Failures in Chinese Large Language Models (2026.acl-long)
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| Challenge: | Existing studies suggest that failures of large language models in social contexts are not due to limited linguistic competence, but to inappropriate recognition. |
| Approach: | They propose a framework that decomposes social adaptation into three orthogonal dimensions and conduct controlled comparisons across multiple Chinese LLMs under implicit and explicit conditions. |
| Outcome: | The proposed framework decomposes social adaptation into three orthogonal dimensions and conducts controlled comparisons across multiple Chinese LLMs under implicit and explicit conditions. |
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering (2024.emnlp-main)
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Haoyu Wang, Ruirui Li, Haoming Jiang, Jinjin Tian, Zhengyang Wang, Chen Luo, Xianfeng Tang, Monica Cheng, Tuo Zhao, Jing Gao
| Challenge: | Retrieval-augmented Large Language Models struggle with complex inputs and noisy knowledge retrieval hindering model effectiveness. |
| Approach: | They propose a query generation method that integrates query generation blending with knowledge filtering to enhance retrieval-augmented LLMs. |
| Outcome: | The proposed approach surpasses state-of-the-art benchmarks on open-domain question answering benchmarks. |
Do Multi-hop Readers Dream of Reasoning Chains? (D19-58)
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| Challenge: | Existing models for multihop reasoning are limited in their performance . multi-hop reasoning requires the ability to gather information from multiple passages . |
| Approach: | They propose a method that provides the full reasoning chain of multiple passages instead of just one final passage where the answer appears. |
| Outcome: | The proposed model improves on existing models by providing the full reasoning chain of multiple passages instead of just one final passage where the answer appears. |
Depth Growing for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation models with tens and even more than a hundred blocks have shown effectiveness in image recognition. |
| Approach: | They propose a two-stage approach with three specially designed components to construct deeper NMT models. |
| Outcome: | The proposed approach improves on WMT14 EnglishGerman and EnglishFrench translation tasks. |
Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference (2025.naacl-long)
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Zeping Li, Xinlong Yang, Ziheng Gao, Ji Liu, Guanchen Li, Zhuang Liu, Dong Li, Jinzhang Peng, Lu Tian, Emad Barsoum
| Challenge: | Existing methods such as Medusa lack adequate information interaction between different drafting heads. |
| Approach: | They propose an enhanced speculative decoding framework that builds upon Medusa and integrates a drafting block capable of parallel inference. |
| Outcome: | The proposed framework outperforms Medusa in terms of head accuracy and latency. |
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 . |
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue (2022.emnlp-main)
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| Challenge: | Existing generative replay methods use only a single task-specific token to control their models. |
| Approach: | They propose a method to capture task-specific distributions with a conditional variational autoencoder, conditioned on natural language prompts to guide the pseudo-sample generation. |
| Outcome: | The proposed method outperforms baselines on natural language understanding tasks of advanced task-oriented dialogue (ToD) systems. |
McBE: A Multi-task Chinese Bias Evaluation Benchmark for Large Language Models (2025.findings-acl)
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| Challenge: | Existing datasets on bias evaluation for large language models focus on English and North American culture and are limited to one task. |
| Approach: | They propose to evaluate Chinese language models' biases from multiple perspectives using a multi-task Chinese Bias Evaluation Benchmark. |
| Outcome: | The proposed model covers 12, 82 subcategories and 5 evaluation tasks covering a wide range of categories and content diversity. |
CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation (2024.acl-long)
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| Challenge: | CharacterEval is a benchmark for comprehensive RPCA assessment in Chinese . authors show that Chinese LLMs exhibit more promising capabilities than GPT-4 in role-playing conversation. |
| Approach: | They propose a Chinese benchmark for comprehensive RPCA assessment . they use a dataset of Chinese role-playing dialogues and character profiles . |
| Outcome: | The proposed benchmark demonstrates that Chinese LLMs exhibit more promising capabilities than GPT-4 in Chinese role-playing conversation. |
F²Bench: An Open-ended Fairness Evaluation Benchmark for LLMs with Factuality Considerations (2025.emnlp-main)
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| Challenge: | Existing fairness evaluation benchmarks for large language models rely on closed-ended evaluation formats that overlook factuality considerations rooted in historical, social, physiological, and cultural contexts. |
| Approach: | They propose an open-ended fairness evaluation benchmark for large language models . they incorporate factuality considerations and multi-turn reasoning into the benchmark . |
| Outcome: | The proposed benchmark incorporates factual grounding and text generation to better reflect the complexities of real-world model usage. |
Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding (2026.findings-acl)
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Weixu Zhang, Fanghua Ye, Qiang Gao, Jian Li, Haolun Wu, Yuxing Tian, Sijing Duan, Nan Du, Xiaolong Li
| Challenge: | Large language models produce content that contradicts or overlooks information provided in the input context, a phenomenon known as faithfulness hallucination. |
| Approach: | They propose a lightweight framework that boosts the generation probability of context-relevant tokens by boosting the generation of tokens. |
| Outcome: | The proposed framework improves faithfulness metrics with minimal generation overhead. |
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering (D19-58)
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Rajarshi Das, Ameya Godbole, Dilip Kavarthapu, Zhiyu Gong, Abhishek Singhal, Mo Yu, Xiaoxiao Guo, Tian Gao, Hamed Zamani, Manzil Zaheer, Andrew McCallum
| Challenge: | Multi-hop question answering (QA) requires an information retrieval system that can find multiple supporting evidence needed to answer the question. |
| Approach: | They propose a technique that uses information of entities present in the initial retrieved evidence to learn to ‘hop’ onto other relevant evidence. |
| Outcome: | The proposed method boosts retrieval performance on a multi-hop question answering dataset with 5 million Wikipedia paragraphs and a model without training increases its performance by 10.59 F1. |
Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language (2026.findings-acl)
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Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li, Wei Chen, Xu Liu, null Aruukhan, Xiangdong Su, Hongxu Hou, Guanglai Gao
| Challenge: | Current state-of-the-art LLMs exhibit clear limitations on multiple tasks, while performing well on tasks that involve contextual semantic understanding. |
| Approach: | They propose a mouse-based benchmark to evaluate LLMs' performance on NLP tasks involving Chouxiang Language. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs on six NLP tasks involving Chouxiang Language. |
Task-oriented Word Embedding for Text Classification (C18-1)
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| Challenge: | Existing word embeddings only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. |
| Approach: | They propose a task-oriented word embedding method that regularizes the distribution of words to enable a clear classification boundary. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on a text classification task. |
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)
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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun
| Challenge: | Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically. |
| Approach: | They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance. |
| Outcome: | The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs. |
Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport (2021.emnlp-main)
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| Challenge: | Existing methods for timeline summarization ignore the events’ intra-structures and inter-structure connections. |
| Approach: | They propose to represent news articles as an event-graph, thus compressing the whole graph to its salient sub-graph. |
| Outcome: | The proposed method significantly improves on the state-of-the-art on three real-world datasets, including two public benchmarks and a Timeline100 dataset. |
Self-adaptive Context and Modal-interaction Modeling For Multimodal Emotion Recognition (2023.findings-acl)
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| Challenge: | Existing methods to predict emotion label for a given utterance lack modeling of diverse dependency ranges and inconsistent treatment of contribution for various modalities. |
| Approach: | They propose a multimodal emotion recognition in conversation task that uses context and multiple modalities to predict emotion label for a given utterance. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on three multimodal datasets. |
Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation (P19-1)
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| Challenge: | Existing benchmarks measure common sense knowledge indirectly or without reasoning. |
| Approach: | They propose a benchmark to test whether a system can differentiate natural language statements that make sense from those that do not make sense. |
| Outcome: | The proposed benchmarks show that models trained on large corpora perform better than humans on some benchmarks. |
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning (2020.findings-emnlp)
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| Challenge: | Existing work on knowledge graphs infers a missing relationship between entities with a multi-hop rule . Empirical results show that our multi-chain multi-homing (MCMH) rules yield superior results compared to the standard single-chain approaches. |
| Approach: | They propose to use a generalized form of multi-hop rules to learn generalized rules efficiently . they propose to select a small set of relation chains as a rule and evaluate confidence . |
| Outcome: | The proposed method outperforms the existing methods and the existing frameworks. |
Emotion Trajectory-aware Retrieval for Markov-driven Emotion Anticipation in LLM-based Emotional Support Conversation (2026.findings-acl)
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| Challenge: | Existing strategies focus on planning the next-turn dialogue strategies, while external strategy planners focus on generating empathetic responses. |
| Approach: | They propose a Markov-driven emotion anticipation framework with emotion trajectory-aware retrieval for LLM-based ESC, which anticipates future emotion states to guide strategy planning and achieve sustained emotional support. |
| Outcome: | The proposed framework can anticipate future emotions and achieve sustained emotional support on two datasets with two models. |
CEDAR: A Chinese Evaluation Dataset for Computational Argumentation (2026.acl-long)
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| Challenge: | Existing debate datasets neglect important labels for argument mining, generation, and evaluation. |
| Approach: | They propose a Chinese Evaluation Dataset for Computational Argumentation that includes key arguments and key rhetorical figures, debater roles, modal words, debate results and transcripts. |
| Outcome: | The proposed dataset covers 600 debates about 318 topics from Chinese debate competitions. |
GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning (2026.findings-acl)
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Kaiyuan Tian, Linbo Qiao, Yu Tang, Gongqingjian Jiang, Baihui Liu, Yifu Gao, Xialin Su, Dongsheng Li
| Challenge: | Low-rank adaptation methods for large language models limit expressiveness and performance . layer-wise fine-tuning methods overlook variations in layer importance across tasks and training stages, resulting in suboptimal performance on downstream tasks. |
| Approach: | They propose a gradient-based adaptive layer-wise importance sampling framework that updates only a subset of parameters to reduce memory usage. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in accuracy and memory usage. |
Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning (2026.acl-long)
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Junpeng Ding, Zichen Tang, Haihong E, Mengyuan Ji, Yang Liu, Haolin Tian, Haiyang Sun, Pengqi Sun, Yang Xu, Yichen Liu, Haocheng Gao, Zijie Xi, Ruomeng Jiang, Peizhi Zhao, Rongjin Li, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Jintong Chen, Siying Lin
| Challenge: | Xu and Peng, 2025) . . SPUR is a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images. |
| Approach: | They propose to use 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images to evaluate the visual perception of multimodal large language models (MLLMs) . they also propose to utilize cross-panel relation understanding to evaluate MLLM’s ability to decipher intricate cross-panel relations. |
| Outcome: | The proposed model is based on 4,264 question-answering pairs derived from 1,084 expert-curated images. |
Born Pragmatic, Trained to Hallucinate? Quantifying the Origins of Contextual Bias in LLMs via the PaCE Benchmark (2026.findings-acl)
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| Challenge: | Large language models excel at capturing communicative intent, but they have a side effect: pragmatic hallucination. |
| Approach: | They propose a benchmark to quantify the impact of pragmatic hallucination on large language models . they propose RLHF and SFT to induce a strong tendency for pragmatic over-attribution . |
| Outcome: | The proposed model outperforms existing models in predicting pragmatic hallucinations . the evaluations show that current alignment paradigms lack precise control over pragmatic boundaries . |
Learning Continuous Temporal Dynamics on Symplectic Manifolds for Temporal Knowledge Graph Embedding (2026.findings-acl)
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| Challenge: | Existing methods for temporal knowledge graph embedding lack explicit structural constraints for continuous-time dynamics. |
| Approach: | They propose a Temporal Knowledge Graph Embedding framework that embeds temporal dynamics into a symplectic phase space. |
| Outcome: | The proposed framework achieves competitive performance with lower embedding dimensions. |
Lightweight Haar Wavelet Subband Pruning for LLMs (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive performance but require computational and memory resources. |
| Approach: | They propose a post-training framework that uses a Haar wavelet transform to prune weights. |
| Outcome: | The proposed pruning framework reduces pruning time and computational costs by removing less important weights while preserving model architecture. |
ToMELP: A Theory-of-Mind Benchmark for Route-Controlled Persuasion under the Elaboration Likelihood Model (2026.findings-acl)
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| Challenge: | Theory of Mind (ToM) is widely regarded as central to effective persuasion, yet existing evaluations fail to capture the infer–apply loop that arises in real-world dialogue. |
| Approach: | They propose a benchmark that conditions on the audience persona p and the Elaboration Likelihood Model (ELM) route r within persuasive conversations. |
| Outcome: | The proposed model can model the interlocutor's mental states over multiple turns and adapt strategy and tone accordingly. |
Bridging Kernel Drivers and Virtual Device Models with LLM-Powered Automation (2026.acl-demo)
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| Challenge: | Linux kernel device drivers are tightly coupled with hardware, making them difficult to execute and test without physical devices. |
| Approach: | They present a tool that generates QEMU-based virtual devices directly from Linux driver source code. |
| Outcome: | The proposed tool generates QEMU-based virtual devices directly from Linux driver source code. |
360∘REA: Towards A Reusable Experience Accumulation with 360∘ Assessment for Multi-Agent System (2024.findings-acl)
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| Challenge: | Recent studies focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks. |
| Approach: | They propose a hierarchical multi-agent framework that uses 360 assessment to accumulate experience through fine-grained assessment. |
| Outcome: | The proposed framework is based on corporate organizational practices and employs a dual-level experience pool for agents to accumulate experience through fine-grained assessment. |
Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry (2026.acl-long)
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Jiang Li, Tian Lan, Shanshan Wang, Dongxing Zhang, Dianqing Lin, Guanglai Gao, Derek F. Wong, Xiangdong Su
| Challenge: | a recent study shows that large language models can generate text, but they can also fabricate large amounts of false or misleading content. |
| Approach: | They propose a benchmark to detect LLM-generated classical Chinese poetry . they compare 12 different AI detectors to find out whether a poem is authored by AI . |
| Outcome: | The proposed benchmark compared 12 AI detectors with a dataset of 30,664 Chinese poems . the results highlight the limitations of current Chinese text detectors . |