The history and current state of interpretability taxonomized according to the types of causal units utilized, as well as methods used to search over mediators are described.
Understanding contextual causality is critical for large language models (LLMs), as it enables them to accurately identify causal relations in specific situations and support more reliable decision-making. Despite its significance, a systematic exploration of contextual causality with LLMs is still lacking. To fill thi...
Building language technologies and conducting NLP research for low-resource languages---particularly when led by native speakers or involving participatory research practices---are often framed as means of addressing inequality, serving local communities, and, at times, contributing to *decolonisation*. In this paper,...
This survey reviews LLM interpretability through the lens of actionability, presenting a taxonomy of attributional and mechanistic approaches, along with emerging methods tailored to vision–language models (VLMs), and examining how actionable interpretability supports downstream objectives.
Jie Cai, Mafizur Rahman, James Enouen et al.· Proceedings of the Thirty-Fi...· 1 citation
For decades, public opinion scholars have argued for the need to go beyond measuring isolated political preferences to more richly examine how individuals reason about and justify the interconnections between their preferences. While early efforts used interviews and hand-coding to elicit the network structure of sub...
Sarah Shugars, Xin-Feng Gu· Network Science· 0 citations
These findings show that LLM-assisted peer review changes the functional composition of review text, making it important to distinguish LLM-amplified critique from areas requiring human prioritization and accountable judgement.
YunHong Yang, Mike Thelwall, Guo-Xiu He· 0 citations
By combining data-driven EFA with blind expert interpretation, this framework shows that LLMs frequently operate on statistically opaque mechanisms distinct from human reasoning.
Alona Strugatski, Licol Zeinfeld, Jason Cooper et al.· 0 citations
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