5 citations · 10 across the 12 of their papers we have counts for
6 papers · 1 filter
Story Imprinting: AI Assistants Absorb Traits from Human Characters They Resemble
Jorio Cocola, Lev McKinney, Harry Mayne +2
Language models are trained to implement a helpful AI Assistant character (e.g., Claude). We explore how finetuning on synthetic stories affects this character. Does it change the…
Value Leakage: An LLM's Answers Are Silently Shaped by Its Own Values
Jan Betley, Johannes Treutlein, Jan Dubiński +5
People use language models for practical questions whose answers are difficult to verify. We show that models exhibit covert value leakage: the information they provide is influenc…
LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations
Harry Mayne, Ryan Othniel Kearns, Yushi Yang +4
To collaborate effectively with humans, language models must be able to explain their decisions in natural language. We study a specific type of self-explanation: self-generated co…
Can sparse autoencoders be used to decompose and interpret steering vectors?
Harry Mayne, Yushi Yang, Adam Mahdi
Steering vectors are a promising approach to control the behaviour of large language models. However, their underlying mechanisms remain poorly understood. While sparse autoencoder…
How Does DPO Reduce Toxicity? A Mechanistic Neuron-Level Analysis
Yushi Yang, Filip Sondej, Harry Mayne +2
Safety fine-tuning algorithms reduce harmful outputs in language models, yet their mechanisms remain under-explored. Direct Preference Optimization (DPO) is a popular choice of alg…
Unsupervised Learning Approaches for Identifying ICU Patient Subgroups: Do Results Generalise?
Harry Mayne, Guy Parsons, Adam Mahdi
The use of unsupervised learning to identify patient subgroups has emerged as a potentially promising direction to improve the efficiency of Intensive Care Units (ICUs). By identif…