most citedExploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

89 citations · 114 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL20237 cited

A Survey on Detection of LLMs-Generated Content

Xianjun Yang, Liangming Pan, Xuandong Zhao +4

The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of s…

cs.CL20234 cited

TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models

Xiao Wang, Yuansen Zhang, Tianze Chen +9

Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. However, the continual learning aspect of th…

cs.CL20233 cited

Zero-Shot Detection of Machine-Generated Codes

Xianjun Yang, Kexun Zhang, Haifeng Chen +3

This work proposes a training-free approach for the detection of LLMs-generated codes, mitigating the risks associated with their indiscriminate usage. To the best of our knowledge…

cs.CV20238 cited

LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation

Yujie Lu, Xianjun Yang, Xiujun Li +2

Existing automatic evaluation on text-to-image synthesis can only provide an image-text matching score, without considering the object-level compositionality, which results in poor…

cs.CL20233 cited

Dynamic Prompting: A Unified Framework for Prompt Tuning

Xianjun Yang, Wei Cheng, Xujiang Zhao +3

It has been demonstrated that the art of prompt tuning is highly effective in efficiently extracting knowledge from pretrained foundation models, encompassing pretrained language m…

cs.CL202389 cited

Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

Xianjun Yang, Yan Li, Xinlu Zhang +2

Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining…