Papers by Cong Shen
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning (2024.findings-acl)
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| Challenge: | Existing methods to select unlabeled examples for annotation require a long time due to their complexity, hindering their practical viability. |
| Approach: | They propose a graph-based selection method to efficiently identify high-quality instances while minimizing computational overhead. |
| Outcome: | The proposed method significantly reduces selection time and improves performance on different tasks. |
Focusing Condition: Inference-Time Self-Contrastive Steering Elicits Better Conditional Text Embeddings in LLMs (2026.acl-long)
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| Challenge: | Existing methods for extracting conditional text embeddings from large language models (LLMs) relying on prompts often fails to produce high-quality conditional embeddables, resulting in degradation of quality. |
| Approach: | They propose a plug-and-play method that constructs unconditional general text embeddings and uses them to refine conditional text embeds. |
| Outcome: | The proposed method improves performance of prompt-based methods on clustering, Semantic Textual Similarity, and triplet alignment datasets. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism (2026.acl-long)
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| Challenge: | Parallel Speculative Decoding (PSD) has limitations due to speedup limits and high computational waste . a novel synchronous mechanism solves the Retrieval Precision-Efficiency Dilemma . |
| Approach: | They propose a framework that combines a draft-verification-based approach with a synchronous mechanism to solve the Retrieval Precision-Efficiency Dilemma. |
| Outcome: | The proposed framework breaks speedup limits for Speculative Decoding by overlapping draft generation with verification. |
From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning (2025.findings-emnlp)
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| Challenge: | In-context learning (ICL) enables large language models to perform novel tasks without parameter updates by conditioning on a few input-output examples. |
| Approach: | They propose a cost-efficient two-stage pipeline that reduces reliance on LLMs for data labeling. |
| Outcome: | The proposed pipeline reduces reliance on LLMs for data labeling . it leverages readily available cross-task examples to prompt an LLM and pseudo-label a small set of target task instances. |
Training ELECTRA Augmented with Multi-word Selection (2021.findings-acl)
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| Challenge: | Existing pre-training methods for NLP tasks require massive computation resources. |
| Approach: | They propose a method that trains a discriminator to detect replaced tokens and select original tokens from candidate sets. |
| Outcome: | The proposed method improves ELECTRA based on multi-task learning on GLUE and SQUAD datasets. |
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)
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Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen
| Challenge: | Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer. |
| Approach: | They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs). |
| Outcome: | The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation. |
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data. |
| Approach: | They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy. |
| Outcome: | The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks. |