1 citations · 3 across the 9 of their papers we have counts for
9 papers
Probe Generalization as Subspace Selection for OOD Deception Detection
Daniel Yoo, Adrians Skapars
Linear probes can be used to detect behaviors and concepts inside language model activations, but may fail to transfer to out-of-distribution examples. When studying the generaliza…
BLOOM-WILT: Logit Tilting for Behaviour Elicitation in Automated LLM Auditing
Adrians Skapars, Edoardo Manino
Users of a deployed language model routinely encounter behaviours that have never been observed during testing, since deployment puts the model through orders of magnitude more int…
Do Linear Probes Generalize Better in Persona Coordinates?
Prasad Mahadik, Adrians Skapars
It is becoming increasingly necessary to have monitors check for harmful behaviors during language model interactions, but text-only monitoring has not been sufficient. This is bec…
The Impact of Off-Policy Training Data on Probe Generalisation
Nathalie Kirch, Samuel Dower, Adrians Skapars +3
Probing has emerged as a promising method for monitoring large language models (LLMs), enabling cheap inference-time detection of concerning behaviours. However, natural examples o…
GPT, But Backwards: Exactly Inverting Language Model Outputs
Adrians Skapars, Edoardo Manino, Youcheng Sun +1
Recent work shows that distinct inputs produce distinct hidden representations in LLMs, so the input can in principle be recovered from them. Yet, no existing method can reliably d…
Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
Daniel Koh, Yannic Noller, Corina S. Pasareanu +2
Large language models (LLMs) have demonstrated strong performance on coding tasks such as generation, completion and repair, but their ability to handle complex symbolic reasoning…
SAFLITE: Fuzzing Autonomous Systems via Large Language Models
Taohong Zhu, Adrians Skapars, Fardeen Mackenzie +4
Fuzz testing effectively uncovers software vulnerabilities; however, it faces challenges with Autonomous Systems (AS) due to their vast search spaces and complex state spaces, whic…
Transformers Use Causal World Models in Maze-Solving Tasks
Alex F. Spies, William Edwards, Michael I. Ivanitskiy +5
Recent studies in interpretability have explored the inner workings of transformer models trained on tasks across various domains, often discovering that these networks naturally d…
Was it Slander? Towards Exact Inversion of Generative Language Models
Adrians Skapars, Edoardo Manino, Youcheng Sun +1
Training large language models (LLMs) requires a substantial investment of time and money. To get a good return on investment, the developers spend considerable effort ensuring tha…