1 citations · 1 across the 9 of their papers we have counts for
Showing cs.CLShow all
3 papers · 1 filter
cs.CL2025
Entropy-Guided Token Dropout: Training Autoregressive Language Models with Limited Domain Data
Jiapeng Wang, Yiwen Hu, Yanzipeng Gao +7
As access to high-quality, domain-specific data grows increasingly scarce, multi-epoch training has become a practical strategy for adapting large language models (LLMs). However,…
cs.CL2025
TALENT: Table VQA via Augmented Language-Enhanced Natural-text Transcription
Guo Yutong, Wanying Wang, Yue Wu +2
Table Visual Question Answering (Table VQA) is typically addressed by large vision-language models (VLMs). While such models can answer directly from images, they often miss fine-g…
cs.CL2025
LLMs Can Generate a Better Answer by Aggregating Their Own Responses
Zichong Li, Xinyu Feng, Yuheng Cai +6
Large Language Models (LLMs) have shown remarkable capabilities across tasks, yet they often require additional prompting techniques when facing complex problems. While approaches…