Papers by Rui Gong
Graph-Structured Speculative Decoding (2024.findings-acl)
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Zhuocheng Gong, Jiahao Liu, Ziyue Wang, Pengfei Wu, Jingang Wang, Xunliang Cai, Dongyan Zhao, Rui Yan
| Challenge: | Speculative decoding is a promising technique to accelerate the inference of Large Language Models. |
| Approach: | They propose a method that uses a token graph to record multiple sequence hypotheses within a single draft stage. |
| Outcome: | The proposed method significantly accelerates the inference of Large Language Models (LLMs) it allows the LLM to choose from and select the longest sequence that meets its standards. |
Cross-Domain Review Generation for Aspect-Based Sentiment Analysis (2021.findings-acl)
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| Challenge: | Existing domain adaptation methods for Aspect-Based Sentiment Analysis lack finegrained labeled data. |
| Approach: | They propose a new domain adaptation paradigm called cross-domain review generation which aims to generate target-domain reviews with fine-grained annotation based on the labeled source domain. |
| Outcome: | The proposed approach is superior to state-of-the-art domain adaptation methods. |
Improving Input-label Mapping with Demonstration Replay for In-context Learning (2023.findings-emnlp)
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| Challenge: | In-context learning (ICL) is an emerging capability of large autoregressive language models where a few demonstrations are appended to the input to enhance the model’s understanding of downstream NLP tasks without directly adjusting the model parameters. |
| Approach: | They propose a method where a few demonstrations are appended to the input to enhance the model's understanding of downstream NLP tasks without directly adjusting the model parameters. |
| Outcome: | The proposed method significantly improves the input-label mapping in ICL demonstrations. |
Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules (2024.emnlp-main)
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| Challenge: | Empirical results show that MoMs consistently outperform vanilla transformers . |
| Approach: | They propose an architecture that allows for a mixture-of-modules computation that uses a finite set of modules defined by multi-head attention and feed-forward networks. |
| Outcome: | The proposed architecture outperforms vanilla Transformers and their variants in multiple ways. |
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing methods for Named Entity Recognition (NER) use a similarity metric to measure semantic similarity between test samples and referents, but their performance is limited due to the label scarcity. |
| Approach: | They propose a novel approach to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class. |
| Outcome: | The proposed approach outperforms state-of-the-art models with a significant margin in most cases. |
Finding the Dominant Winning Ticket in Pre-Trained Language Models (2022.findings-acl)
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| Challenge: | Existing studies on pre-trained language models show that they can fine-tune parameters but achieve good downstream performance. |
| Approach: | They find that a dominant winning ticket takes up 0.05% of the parameters and is transferable across different tasks. |
| Outcome: | The proposed model can achieve comparable performance with the full-parameter model, the authors show . the dominant winning ticket takes up 0.05% of the parameters, and the model is transferable across tasks, they show - the authors conclude . |
TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems (2026.findings-acl)
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| Challenge: | Existing communication topologies rely on spatio-temporal dialogues, which incur high latency and computation. |
| Approach: | They propose a framework for one-shot Topology generation with Diverse Interaction Modes that enables agents to construct heterogeneous communication without iterative coordination. |
| Outcome: | The proposed framework reduces total token consumption by 46.41% while improving average performance by 1.50% over state-of-the-art methods. |
Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)
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| Challenge: | Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task. |
| Approach: | They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features . |
| Outcome: | The proposed method improves on the state-of-the-art in two aspects of the ABSA task. |
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models (2023.findings-acl)
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Zhuocheng Gong, Jiahao Liu, Qifan Wang, Yang Yang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Rui Yan
| Challenge: | Quantization is a viable solution for pre-trained language models, but most existing methods are task-specific and require customized training and quantization with a large number of trainable parameters. |
| Approach: | They propose a "quantize before fine-tuning" framework that allows for quantization with a large number of trainable parameters on each individual task. |
| Outcome: | The proposed framework is compatible with quantization-aware training and post-training quantization and corrects quantization errors. |
Graph Representation Learning in Hyperbolic Space via Dual-Masked (2025.coling-main)
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| Challenge: | Existing MR-based methods do not fully consider deep node and structural information. |
| Approach: | They propose a graph dual-masked self-supervised graph representation learning framework in hyperbolic space that masks nodes and edges and performs node aggregation. |
| Outcome: | The proposed method is superior in downstream tasks such as node classification and link prediction. |