54 citations · 76 across the 6 of their papers we have counts for
6 papers
BELLS: A Framework Towards Future Proof Benchmarks for the Evaluation of LLM Safeguards
Diego Dorn, Alexandre Variengien, Charbel-Raphaël Segerie +1
Input-output safeguards are used to detect anomalies in the traces produced by Large Language Models (LLMs) systems. These detectors are at the core of diverse safety-critical appl…
Look Before You Leap: A Universal Emergent Decomposition of Retrieval Tasks in Language Models
Alexandre Variengien, Eric Winsor
When solving challenging problems, language models (LMs) are able to identify relevant information from long and complicated contexts. To study how LMs solve retrieval tasks in div…
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model
Michael Hanna, Ollie Liu, Alexandre Variengien
Pre-trained language models can be surprisingly adept at tasks they were not explicitly trained on, but how they implement these capabilities is poorly understood. In this paper, w…
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
Kevin Wang, Alexandre Variengien, Arthur Conmy +2
Research in mechanistic interpretability seeks to explain behaviors of machine learning models in terms of their internal components. However, most previous work either focuses on…
Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent
Alexandre Variengien, Stefano Nichele, Tom Glover +1
Neural cellular automata (Neural CA) are a recent framework used to model biological phenomena emerging from multicellular organisms. In these systems, artificial neural networks a…
A journey in ESN and LSTM visualisations on a language task
Alexandre Variengien, Xavier Hinaut
Echo States Networks (ESN) and Long-Short Term Memory networks (LSTM) are two popular architectures of Recurrent Neural Networks (RNN) to solve machine learning task involving sequ…