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20232026
most citedCompressing Context to Enhance Inference Efficiency of Large Language Models

3 citations · 11 across the 13 of their papers we have counts for

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10 papers · 1 filter

cs.CL2026

Reading Between the Frames: Interpreting Implicit and Non-literal Meaning in Social Media Videos

Yang Wang, Yanan Ma, Yiqi Liu +7

Social media videos often communicate meanings that go beyond their visible actions, captions, or speech. A mundane clip may become humorous, ironic, or satire only through the int…

cs.CL2025

Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders

Shun Wang, Tyler Loakman, Youbo Lei +5

Large Language Models (LLMs) are traditionally viewed as black-box algorithms, therefore reducing trustworthiness and obscuring potential approaches to increasing performance on do…

cs.CL2025

DRE: An Effective Dual-Refined Method for Integrating Small and Large Language Models in Open-Domain Dialogue Evaluation

Kun Zhao, Bohao Yang, Chen Tang +4

Large Language Models (LLMs) excel at many tasks but struggle with ambiguous scenarios where multiple valid responses exist, often yielding unreliable results. Conversely, Small La…

cs.CL2025

Does Table Source Matter? Benchmarking and Improving Multimodal Scientific Table Understanding and Reasoning

Bohao Yang, Yingji Zhang, Dong Liu +2

Recent large language models (LLMs) have advanced table understanding capabilities but rely on converting tables into text sequences. While multimodal large language models (MLLMs)…

cs.CL2024

CAST: Corpus-Aware Self-similarity Enhanced Topic modelling

Yanan Ma, Chenghao Xiao, Chenhan Yuan +4

Topic modelling is a pivotal unsupervised machine learning technique for extracting valuable insights from large document collections. Existing neural topic modelling methods often…

cs.CL20241 cited

BioMNER: A Dataset for Biomedical Method Entity Recognition

Chen Tang, Bohao Yang, Kun Zhao +4

Named entity recognition (NER) stands as a fundamental and pivotal task within the realm of Natural Language Processing. Particularly within the domain of Biomedical Method NER, th…