Multilingual Unlearning in LLMs: Transfer, Dynamics, and Reversibility
Chaoyi Xiang · Olga Ohrimenko · Benjamin Rubinstein · Lea Frermann
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Chaoyi Xiang · Olga Ohrimenko · Benjamin Rubinstein · Lea Frermann
Yanbei Jiang · Amr Keleg · Ryandito Diandaru · Jey Han Lau · Lea Frermann · Biaoyan Fang · Fajri Koto
While the real world is inherently stochastic, Large Language Models (LLMs) are predominantly evaluated on single-round inference against fixed ground truths. In this work, we shift the lens to distribution alignment: assessing whether LLMs, when prompted repeatedly, can generate outputs that adh...
Ming-Bin Chen · Jey Han Lau · Lea Frermann
Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conversation. We introduce a framework for Conversational Information Gain (CIG) that evaluates each utterance in terms of how it advances collective...
Matteo Guida · Yulia Otmakhova · Eduard Hovy · Lea Frermann
Framing theory posits that how information is presented shapes audience responses, but computational work has largely ignored audience reactions. While recent work has shown that article framing systematically shapes the content of reader responses, this paper asks: does framing also affect respo...
Yulia Otmakhova · Matteo Guida · Lea Frermann
Metaphors are powerful framing devices, yet their source domains alone do not fully explain the specific associations they evoke. We argue that the interplay between source domains and semantic frames determines how metaphors shape understanding of complex issues, and present a computational fram...