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20162024
most citedFactuality Challenges in the Era of Large Language Models

163 citations · 753 across the 71 of their papers we have counts for

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Showing 2023 · cs.CLShow all

14 papers · 2 filters

cs.CL2023

PHD: Pixel-Based Language Modeling of Historical Documents

Nadav Borenstein, Phillip Rust, Desmond Elliott +1

The digitisation of historical documents has provided historians with unprecedented research opportunities. Yet, the conventional approach to analysing historical documents involve…

cs.CL2023★ 1 cited

The Causal Influence of Grammatical Gender on Distributional Semantics

Karolina Stańczak, Kevin Du, Adina Williams +2

How much meaning influences gender assignment across languages is an active area of research in linguistics and cognitive science. We can view current approaches as aiming to deter…

cs.CL2023★ 5 cited

Social Bias Probing: Fairness Benchmarking for Language Models

Marta Marchiori Manerba, Karolina Stańczak, Riccardo Guidotti +1

While the impact of social biases in language models has been recognized, prior methods for bias evaluation have been limited to binary association tests on small datasets, limitin…

cs.CL2023★ 3 cited

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…

cs.CL2023

People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection

Indira Sen, Dennis Assenmacher, Mattia Samory +3

NLP models are used in a variety of critical social computing tasks, such as detecting sexist, racist, or otherwise hateful content. Therefore, it is imperative that these models a…

cs.CL2023

Explaining Interactions Between Text Spans

Sagnik Ray Choudhury, Pepa Atanasova, Isabelle Augenstein

Reasoning over spans of tokens from different parts of the input is essential for natural language understanding (NLU) tasks such as fact-checking (FC), machine reading comprehensi…