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...· 0 citations
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
Large language models (LLMs) are increasingly discussed as tools for peer review, but their value is often assessed through human-likeness, perceived usefulness, or textual overlap with reviewer comments. This study shifts attention from whether LLMs resemble human reviewers to what functions of scientific critique the...
YunHong Yang, Mike Thelwall, Guo-Xiu He· 0 citations
The evaluation of large language models (LLMs) relies heavily on human-designed assessments, implicitly assuming that AI and humans employ similar underlying cognitive constructs. Challenging this assumption, we investigate whether the latent factors governing LLM performance carry the same substantive, human-interpret...
Alona Strugatski, Licol Zeinfeld, Jason Cooper et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.