5 citations · 40 across the 105 of their papers we have counts for
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Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid +10
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs. In this work, we…
A Survey of Confidence Estimation and Calibration in Large Language Models
Jiahui Geng, Fengyu Cai, Yuxia Wang +3
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks in various domains. Despite their impressive performance, they can be unreliable…
How are Prompts Different in Terms of Sensitivity?
Sheng Lu, Hendrik Schuff, Iryna Gurevych
In-context learning (ICL) has become one of the most popular learning paradigms. While there is a growing body of literature focusing on prompt engineering, there is a lack of syst…
A Template Is All You Meme
Luke Bates, Peter Ebert Christensen, Preslav Nakov +1
Templatic memes, characterized by a semantic structure adaptable to the creator's intent, represent a significant yet underexplored area within meme processing literature. With the…
Measuring Pointwise -Usable Information In-Context-ly
Sheng Lu, Shan Chen, Yingya Li +3
In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models. In this work, we adapt a recently proposed hard…