most citedAn Empirical Study on Information Extraction using Large Language Models

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2025

Extending Automatic Machine Translation Evaluation to Book-Length Documents

Kuang-Da Wang, Shuoyang Ding, Chao-Han Huck Yang +4

Despite Large Language Models (LLMs) demonstrating superior translation performance and long-context capabilities, evaluation methodologies remain constrained to sentence-level ass…

cs.SD2025

WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations

Jaeyeon Kim, Heeseung Yun, Sang Hoon Woo +2

Large audio language models (LALMs) extend language understanding into the auditory domain, yet their ability to perform low-level listening, such as pitch and duration detection,…

cs.AI2025

Think Twice, Act Once: A Co-Evolution Framework of LLM and RL for Large-Scale Decision Making

Xu Wan, Wenyue Xu, Chao Yang +1

Recent advancements in Large Language Models (LLMs) and Reinforcement Learning (RL) have shown significant promise in decision-making tasks. Nevertheless, for large-scale industria…

cs.CL2025

C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation

Guoxin Chen, Minpeng Liao, Peiying Yu +5

Retrieval-augmented generation (RAG) systems face a fundamental challenge in aligning independently developed retrievers and large language models (LLMs). Existing approaches typic…

cs.CL20242 cited

An Empirical Study on Information Extraction using Large Language Models

Ridong Han, Chaohao Yang, Tao Peng +4

Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (…