16 citations · 26 across the 14 of their papers we have counts for
6 papers · 1 filter
LocUS: Head Selection and Subspace Projection for Targeted Activation Steering
Irene Tallini, Lorenzo Basile, Valentino Maiorca +2
Activation steering is a powerful training-free paradigm for controlling large language models at inference time. However, standard approaches estimate a per-layer steering directi…
Are LLMs Good Safety Agents or a Propaganda Engine?
Neemesh Yadav, Francesco Ortu, Jiarui Liu +5
Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a reflection of its safety policies…
The Intrinsic Dimension of Prompts in Internal Representations of Large Language Models
Karthik Viswanathan, Yuri Gardinazzi, Giada Panerai +2
We study the geometry of token representations at the prompt level in large language models through the lens of intrinsic dimension. Viewing transformers as mean-field particle sys…
Persistent Topological Features in Large Language Models
Yuri Gardinazzi, Karthik Viswanathan, Giada Panerai +3
Understanding the decision-making processes of large language models is critical given their widespread applications. To achieve this, we aim to connect a formal mathematical frame…
The representation landscape of few-shot learning and fine-tuning in large language models
Diego Doimo, Alessandro Serra, Alessio Ansuini +1
In-context learning (ICL) and supervised fine-tuning (SFT) are two common strategies for improving the performance of modern large language models (LLMs) on specific tasks. Despite…
Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals
Francesco Ortu, Zhijing Jin, Diego Doimo +3
Interpretability research aims to bridge the gap between empirical success and our scientific understanding of the inner workings of large language models (LLMs). However, most exi…