201 citations · 1k across the 85 of their papers we have counts for
14 papers · 1 filter
Survey of Cultural Awareness in Language Models: Text and Beyond
Siddhesh Pawar, Junyeong Park, Jiho Jin +7
Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ens…
Can Transformers Learn -gram Language Models?
Anej Svete, Nadav Borenstein, Mike Zhou +2
Much theoretical work has described the ability of transformers to represent formal languages. However, linking theoretical results to empirical performance is not straightforward…
FLARE: Faithful Logic-Aided Reasoning and Exploration
Erik Arakelyan, Pasquale Minervini, Pat Verga +2
Modern Question Answering (QA) and Reasoning approaches based on Large Language Models (LLMs) commonly use prompting techniques, such as Chain-of-Thought (CoT), assuming the result…
Claim Verification in the Age of Large Language Models: A Survey
Alphaeus Dmonte, Roland Oruche, Marcos Zampieri +2
The large and ever-increasing amount of data available on the Internet coupled with the laborious task of manual claim and fact verification has sparked the interest in the develop…
DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models
Sara Vera Marjanović, Haeun Yu, Pepa Atanasova +3
Knowledge-intensive language understanding tasks require Language Models (LMs) to integrate relevant context, mitigating their inherent weaknesses, such as incomplete or outdated k…
Revealing Fine-Grained Values and Opinions in Large Language Models
Dustin Wright, Arnav Arora, Nadav Borenstein +3
Uncovering latent values and opinions embedded in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, this has been approached by prompting…
Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language
Amalie Brogaard Pauli, Isabelle Augenstein, Ira Assent
We are exposed to much information trying to influence us, such as teaser messages, debates, politically framed news, and propaganda - all of which use persuasive language. With th…
Evaluating Input Feature Explanations through a Unified Diagnostic Evaluation Framework
Jingyi Sun, Pepa Atanasova, Isabelle Augenstein
Explaining the decision-making process of machine learning models is crucial for ensuring their reliability and transparency for end users. One popular explanation form highlights…
SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages
Gayane Ghazaryan, Erik Arakelyan, Pasquale Minervini +1
Question Answering (QA) datasets have been instrumental in developing and evaluating Large Language Model (LLM) capabilities. However, such datasets are scarce for languages other…
What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages
Nadav Borenstein, Anej Svete, Robin Chan +5
What can large language models learn? By definition, language models (LM) are distributions over strings. Therefore, an intuitive way of addressing the above question is to formali…
Understanding Fine-grained Distortions in Reports of Scientific Findings
Amelie Wührl, Dustin Wright, Roman Klinger +1
Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasin…
Investigating Human Values in Online Communities
Nadav Borenstein, Arnav Arora, Lucie-Aimée Kaffee +1
Studying human values is instrumental for cross-cultural research, enabling a better understanding of preferences and behaviour of society at large and communities therein. To stud…
Investigating the Impact of Model Instability on Explanations and Uncertainty
Sara Vera Marjanović, Isabelle Augenstein, Christina Lioma
Explainable AI methods facilitate the understanding of model behaviour, yet, small, imperceptible perturbations to inputs can vastly distort explanations. As these explanations are…
Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models
Erik Arakelyan, Zhaoqi Liu, Isabelle Augenstein
Recent studies of the emergent capabilities of transformer-based Natural Language Understanding (NLU) models have indicated that they have an understanding of lexical and compositi…