Papers by Chuan Shi
Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training (2025.naacl-long)
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| Challenge: | Current PP methods face severe bottlenecks, including pipeline bubbles and memory footprint. |
| Approach: | They propose a sequence-level one-forward-one-backward (1F1B) PP method for training LLMs on long sequences with high throughput and memory efficiency. |
| Outcome: | The proposed method achieves 1.14X training throughput with half memory footprint compared to baseline methods . it trains an LLM with 30B parameters on sequences up to 64k tokens using 64X NVIDIA A100 GPUs . |
Exploring the Potential of Large Language Models for Heterophilic Graphs (2025.naacl-long)
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| Challenge: | Existing approaches for heterophilic graphs overlook rich textual data associated with nodes, which could unlock deeper insights into their heterophilistic contexts. |
| Approach: | They propose a two-stage framework to enhance node classification on heterophilic graphs by leveraging open-world knowledge encoded by large language models. |
| Outcome: | The proposed framework can be used to better characterize heterophilic graphs, where neighboring nodes often exhibit different labels. |
KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus (2025.findings-naacl)
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Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang, Haitao Leng, Chuan Wang, Qingjie Liu, Wanxiang Che, Yunhong Wang
| Challenge: | Currently, video-based dialogue systems rely on a single dialogue type, hindering their versatility in practical applications. |
| Approach: | They propose to generate video-driven multilingual mixed-type dialogues using KwaiChat . they propose to create a video-based multilingual mix of 4 dialogue types, 30 domains, 4 languages, 13 topics . |
| Outcome: | The proposed model performs best on KwaiChat but is not perfect in this situation. |
MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing (2026.acl-demo)
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| Challenge: | Large language model-based multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. |
| Approach: | They propose a graph-centric framework for orchestrating large language model-based multi-agent systems . they compile a user's natural-language intent into an editable workflow specification and then into an executable graph . |
| Outcome: | The proposed framework compiles natural-language intent into an executable graph and then compile and executes it at runtime. |
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification (D19-1)
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| Challenge: | Existing studies on short text classification focus on long texts and achieve unsatisfactory performance due to the sparsity and limited labeled data. |
| Approach: | They propose a heterogeneous graph neural network based method for semi-supervised short text classification that leverages the full advantage of few labeled data and large unlabeled data through information propagation along the graph. |
| Outcome: | The proposed method outperforms state-of-the-art methods across six benchmark datasets significantly. |
Improving Distantly-Supervised Relation Extraction with Joint Label Embedding (D19-1)
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| Challenge: | Existing methods for relation extraction treat labels as independent and meaningless one-hot vectors, which cause a loss of potential label information for selecting valid instances. |
| Approach: | They propose a multi-layer attention-based model to improve relation extraction with joint label embedding by gating integration and using the embeddable entities as an atten- tion. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods in relation extraction with joint label embedding. |
MidMed: Towards Mixed-Type Dialogues for Medical Consultation (2023.acl-long)
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| Challenge: | Current medical dialogue systems assume that patients have explicit goals but are often unavailable in real-world situations due to the lack of medical knowledge. |
| Approach: | They propose a human-to-human mixed-type medical consultation dialogue corpus . they build benchmarking baselines on MidMed and propose an instruction-guiding framework . Experimental results show the effectiveness of InsMed . |
| Outcome: | The proposed system can help patients clarify their goals in real-world situations . it covers four departments with 8,309 dialogues and provides benchmarking baselines . |
Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases (2025.acl-long)
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| Challenge: | Pretraining language models on formal language can improve their acquisition of natural language, but it is computationally limited. |
| Approach: | They hypothesize that effective transfer occurs when the formal language captures hierarchical dependencies present in natural language and remains within the computational limitations of the model architecture. |
| Outcome: | The proposed model can achieve lower loss on natural language and better generalization with a 33% smaller token budget than training on a matched amount of natural language. |
Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (2021.acl-long)
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| Challenge: | Existing methods for fake news detection rely on linguistic and semantic features from news content and do not exploit external knowledge. |
| Approach: | They propose a graph neural model which compares news to knowledge base through entities for fake news detection. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods on two benchmark datasets. |
Graph Neural News Recommendation with Unsupervised Preference Disentanglement (2020.acl-main)
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| Challenge: | Existing methods to learn informative user and news representations fail to consider high-order connectivity underlying the user-news interactions. |
| Approach: | They propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement which can encode high-order relationships into user and news representations by information propagation along the graph. |
| Outcome: | The proposed model can encode high-order relationships into user and news representations by information propagation along the graph and disentangle latent preference factors by a neighborhood routing algorithm. |
CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding (2026.findings-acl)
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| Challenge: | Existing structure-aware approaches treat structure as serialized text prompts or auxiliary training objectives, failing to provide explicit guidance during inference. |
| Approach: | They propose a plug-and-play method that enhances Large Language Models with Code Graph information through an external, trainable Bridge module. |
| Outcome: | The proposed method decouples structural reasoning from textual generation without updating the backbone. |