1 citations · 1 across the 4 of their papers we have counts for
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AdaptArena: Evaluating Test-Time Personalization of Web Agents
Dongchan Shin, Xing Han Lù, Jiaqi Deng +7
Large language model (LLM) agents have demonstrated strong performance on complex web navigation tasks, yet they remain brittle in real-world settings where user intentions are und…
Operationalising the Superficial Alignment Hypothesis via Task Complexity
Tomás Vergara-Browne, Darshan Patil, Ivan Titov +3
The superficial alignment hypothesis (SAH) posits that large language models learn most of their knowledge during pre-training, and that post-training merely surfaces this knowledg…
Eigenpruning: an Interpretability-Inspired PEFT Method
Tomás Vergara-Browne, Álvaro Soto, Akiko Aizawa
We introduce eigenpruning, a method that removes singular values from weight matrices in an LLM to improve its performance in a particular task. This method is inspired by interpre…