Obtaining human-interpretable explanations of large, general-purpose language models is
an urgent goal for AI safety. However, it is just as important that our interpretability methods …

Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields
of study with significant implications for artificial intelligence. Despite their distinct origins …

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human
performance across various domains. This presents us with an opportunity to further human …

The reliance of text classifiers on spurious correlations can lead to poor generalization at
deployment, raising concerns about their use in safety-critical domains such as healthcare …

The rapid progress of research aimed at interpreting the inner workings of advanced
language models has highlighted a need for contextualizing the insights gained from years …

The field of explainable artificial intelligence emerged in response to the growing need for
more transparent and reliable models. However, using raw features to provide explanations …

Causal explanations of the predictions of NLP systems are essential to ensure safety and
establish trust. Yet, existing methods often fall short of explaining model predictions …

A number of recent benchmarks seek to assess how well models handle natural language
negation. However, these benchmarks lack the controlled example paradigms that would …

Chomsky and others have very directly claimed that large language models (LLMs) are
equally capable of learning languages that are possible and impossible for humans to learn …

Large language models (LLMs) have an impressive ability to draw on novel information
supplied in their context. Yet the mechanisms underlying this contextual grounding remain …