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20212024
most citedExploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

89 citations · 126 across the 15 of their papers we have counts for

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8 papers · 1 filter

cs.CL20241 cited

Uncertainty Quantification for In-Context Learning of Large Language Models

Chen Ling, Xujiang Zhao, Xuchao Zhang +10

In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the…

cs.CL20231 cited

Open-ended Commonsense Reasoning with Unrestricted Answer Scope

Chen Ling, Xuchao Zhang, Xujiang Zhao +7

Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional…

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.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.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…