Papers by Jia-Chen Gu
BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression (2025.findings-naacl)
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| Challenge: | Existing approaches to augment language models with external knowledge but they are limited by static nature of pre-training data. |
| Approach: | They propose a lightweight approach that compresses retrieved documents into highly dense textual summaries to integrate into in-context RAG. |
| Outcome: | The proposed approach reduces latency and costs while achieving high performance in open-domain questions. |
MISP-Meeting: A Real-World Dataset with Multimodal Cues for Long-form Meeting Transcription and Summarization (2025.acl-long)
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| Challenge: | Existing systems that can recognize spoken content, extract key information, and produce concise summaries are lacking in meeting transcription and summarization. |
| Approach: | They propose a multimodal dataset that integrates information from speech, vision, and text modalities to facilitate automatic meeting transcription and summarization (AMTS). |
| Outcome: | The proposed dataset reduces the character error rate (CER) by 36.60% to 20.27% and improves speech recognition and large language models. |
Constraining Sequential Model Editing with Editing Anchor Compression (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) exhibit hallucinations due to incorrect or outdated knowledge embedded in their parameters. |
| Approach: | They propose a framework to constrain the deviation of the parameter matrix during sequential editing by selecting editing anchors that are important in encoding new relations without deviating too much from the original matrix. |
| Outcome: | The proposed framework minimizes deviations caused by model editing while retaining over 70% of the general abilities. |
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners (2025.emnlp-main)
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| Challenge: | Existing knowledge editing methods fail to generalize updates to multi-hop reasoning tasks . Existing methods only edit single or a few model layers, inadequately integrate updated knowledge into reasoning pathways. |
| Approach: | They propose a circuit-aware method that enhances the effective integration of updated knowledge in large language models by leveraging curated data samples guided by their analysis. |
| Outcome: | The proposed method improves accuracy and accuracy of 20% on the MQuAKE dataset while requiring less memory. |
X-ACE: Explainable and Multi-factor Audio Captioning Evaluation (2024.findings-acl)
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| Challenge: | Existing evaluation metrics for automated audio captioning only provide an overall score . current evaluation checklists are inadequate to characterize the nuanced differences . |
| Approach: | They propose an explainable and multi-factor audio captioning evaluation paradigm . they define sound event, source, attribute and relation as four factors tailored for the audio description . |
| Outcome: | The proposed evaluation paradigm improves the quality of audio captions . it can detect mismatches and align with human perception, the authors show . |
Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots (2020.findings-emnlp)
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| Challenge: | Experimental results show that FIRE outperforms previous methods for building knowledge-grounded retrieval-based chatbots . a method called Filtering before iteratively referring is used to ground a conversation on background knowledge . |
| Approach: | They propose a method for grounding conversation on background knowledge . they use context filter and knowledge filter to make context and knowledge aware . experimental results show that FIRE outperforms previous methods . |
| Outcome: | The proposed method outperforms previous methods on two datasets. |
Wider & Closer: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing mainstream methods for zero-shot cross-lingual named entity recognition ignore the rich and complementary information lying in the intermediate layers of pre-trained language models and domain-invariant information is easily lost during transfer. |
| Approach: | They propose a mixture of short-channel distillers to fully interact the rich hierarchical information in the teacher model and to transfer knowledge to the student model sufficiently and efficiently. |
| Outcome: | The proposed method shows great generalization and compatibility across languages and fields. |
BRIEF-Pro: Universal Context Compression with Short-to-Long Synthesis for Fast and Accurate Multi-Hop Reasoning (2026.findings-acl)
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| Challenge: | Experiments show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models. |
| Approach: | They propose a universal, lightweight compressor that distills relevant evidence from retrieved documents into a concise summary for seamless integration into in-context RAG. |
| Outcome: | Experiments on four open-domain multi-hop question-answering datasets show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models. |
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
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| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
| Approach: | They propose a taxonomy for organizing existing LLM-based evaluation metrics and a structured framework to understand and compare them. |
| Outcome: | The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods. |
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations (2022.acl-long)
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| Challenge: | Experimental results show that HeterMPC outperforms various baseline models for response generation in multi-party conversations. |
| Approach: | They propose a heterogeneous graph-based neural network for response generation in multi-party conversations which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Internet Relay Chat (IRC) channel. |
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)
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| Challenge: | Generating natural and informative texts has been a long-standing problem in NLP. |
| Approach: | They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework. |
| Outcome: | The proposed model can learn what and how to generate on two text generation tasks. |
MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation (2023.emnlp-main)
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| Challenge: | Existing methods for multi-party conversations rely on addressee labels and can only be applied to an ideal setting where addresses are missing. |
| Approach: | They propose a method that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. |
| Outcome: | The proposed method outperforms baseline models on Ubuntu IRC channel benchmarks on the task of MPC generation under a common and challenging setting where addressee labels are missing. |
Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue (2024.emnlp-main)
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| Challenge: | Existing methods that edit large language models with updated knowledge can cause side effects on the general abilities of LLMs such as reasoning, natural language inference, and question answering. |
| Approach: | They propose to regularize the edit update weights by imposing constraints on their complexity based on the RElative Change in weighT. |
| Outcome: | The proposed method can significantly mitigate the side effects while maintaining over 94% editing performance. |
Symbolization, Prompt, and Classification: A Framework for Implicit Speaker Identification in Novels (2023.findings-emnlp)
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| Challenge: | Existing methods for speaker identification in novel dialogues are limited to handling explicit narrative patterns and complex cases. |
| Approach: | They propose a framework which identifies implicit speakers in novels via symbolization, prompt, and classification. |
| Outcome: | The proposed framework outperforms existing methods by 4.8% accuracy on the web novel collection, which reduces 47% of speaker identification errors, and outperfies the emerging ChatGPT. |
GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding (2023.acl-long)
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| Challenge: | Existing methods on understanding multi-party conversations typically embed interlocutors and utterances into sequential information flows or use superficial graph structures. |
| Approach: | They propose a plug-and-play method which adapts Transformer-based pre-trained language models for universal MPC understanding. |
| Outcome: | The proposed method can adapt Transformer-based pre-trained language models for universal MPC understanding. |
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding (2021.acl-long)
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| Challenge: | Existing models for multi-party conversation represent interlocutors and utterances individually . existing methods ignore complicated structure of MPC which may provide crucial interlocutor and tertiary semantics. |
| Approach: | They propose a pre-trained model for multi-party conversation that considers learning who says what to whom in a unified model with elaborated self-supervised tasks. |
| Outcome: | The proposed model outperforms existing models on three downstream tasks at two benchmarks. |
Knowledge Mechanisms in Large Language Models: A Survey and Perspective (2024.findings-emnlp)
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Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Using large language models, we can understand knowledge mechanisms in LLMs for learning, storage, utilization, and evolution. |
| Approach: | They propose to analyze knowledge mechanisms in Large Language Models (LLMs) they examine utilization, evolution, and the potential dark knowledge (hypothesis) they hope to help understand knowledge in LLMs and provide insights for future research . |
| Outcome: | The proposed model can be used to analyze the evolution of parametric knowledge in LLMs. |
Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation (2024.emnlp-main)
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| Challenge: | Existing studies show that RALMs generate baseless information or contradicts with the retrieved context. |
| Approach: | They propose a lightweight monitor that leverages fine-grained decoding dynamics to synchronously detect unfaithful sentences. |
| Outcome: | Empirical results show that SynCheck outperforms baseline faithfulness detection and FOD outperformed traditional strategies in terms of faithfulness. |
Knowledge Control for Responsible Generative AI: Bridging Academia, Industry, and Society (2026.acl-tutorials)
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Zheyuan Liu, Yixin Wan, Kai-Wei Chang, Meng Jiang, Jieyu Zhao, Nouha Dziri, Yuning Mao, Jia-Chen Gu, Jindong Gu
| Challenge: | This tutorial introduces the foundations of post-training knowledge control and showcases recent frontier methods. |
| Approach: | This tutorial introduces the foundations of post-training knowledge control and showcases recent frontier methods. |
| Outcome: | This tutorial introduces the foundations of post-training knowledge control and showcases recent frontier methods . key motivations and failure modes, harmful generation and stereotype reinforcement, are addressed . core methods such as machine unlearning, knowledge editing, and inference-time interventions are also included . |
Is ChatGPT a Good Multi-Party Conversation Solver? (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are powerful tools for multi-party conversations, but their capacity to handle multi-parties remains unexplored. |
| Approach: | They propose to evaluate ChatGPT and GPT-4's zero-shot learning capabilities within the context of multi-party conversations (MPCs) they also propose to incorporate MPC structures, encompassing both speaker and addressee architecture. |
| Outcome: | The proposed models perform poorly on a number of MPC tasks while GPT-4 performs well on speaker and addressee architecture. |
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots (D19-1)
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| Challenge: | Existing models for personalized dialogues rank responses according to their semantic relevance with the given context. |
| Approach: | They propose a dually interactive matching network (DIM) for presenting personalities of dialogue agents in retrieval-based chatbots. |
| Outcome: | The proposed model outperforms the existing model by 14.5% and 27.7% on a PERSONA-CHAT dataset. |
Detecting Speaker Personas from Conversational Texts (2021.emnlp-main)
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| Challenge: | Existing studies on personas are pre-defined and hard to obtain before a conversation . a new task aims to detect speaker persona based on conversational text . |
| Approach: | They propose a task to detect speaker personas based on conversational text . they build a dataset for SPD and propose utterance-to-profile matching networks . |
| Outcome: | The proposed task outperforms baseline models and utterance-to-profile (U2P) matching networks. |