activity
20162024
most citedAugGPT: Leveraging ChatGPT for Text Data Augmentation

99 citations · 401 across the 39 of their papers we have counts for

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15 papers · 1 filter

cs.CL2024

Open-domain Implicit Format Control for Large Language Model Generation

Yiqun Yao, Wenjia Ma, Xuezhi Fang +7

Controlling the format of outputs generated by large language models (LLMs) is a critical functionality in various applications. Current methods typically employ constrained decodi…

cs.CL20231 cited

CBSiMT: Mitigating Hallucination in Simultaneous Machine Translation with Weighted Prefix-to-Prefix Training

Mengge Liu, Wen Zhang, Xiang Li +5

Simultaneous machine translation (SiMT) is a challenging task that requires starting translation before the full source sentence is available. Prefix-to-prefix framework is often a…

cs.CL20231 cited

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models

Chengcheng Han, Xiaowei Du, Che Zhang +4

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, i…

cs.CL202330 cited

Radiology-Llama2: Best-in-Class Large Language Model for Radiology

Zhengliang Liu, Yiwei Li, Peng Shu +18

This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning. Radiology-Llama2 is based on the Llama2 arch…

cs.CL202315 cited

CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study

Zihan Guan, Zihao Wu, Zhengliang Liu +5

Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in c…

cs.CL2023

FreeLM: Fine-Tuning-Free Language Model

Xiang Li, Xin Jiang, Xuying Meng +2

Pre-trained language models (PLMs) have achieved remarkable success in NLP tasks. Despite the great success, mainstream solutions largely follow the pre-training then finetuning pa…