565 citations · 2.4k across the 50 of their papers we have counts for
53 papers
The Unequal Influence of Bad Advice: Using Training Data Attribution to Modulate Emergent Misalignment
Gonçalo Paulo, Louis Jaburi, Nora Belrose +2
Fine-tuning large language models on narrow, misaligned tasks can undo their post-training alignment and induce novel misaligned behaviors -- a phenomenon known as \emph{emergent m…
LoRA-Muon: Spectral Steepest Descent on the Low-Rank Manifold
Franz Louis Cesista, Katherine Crowson, Cédric Simal +1
Low-Rank Adaptation (LoRA) significantly reduces compute and memory costs for finetuning Deep Learning models but is often harder to tune than dense training: when using factor-wis…
Bergson: An Open Source Library for Data Attribution
Lucia Quirke, Louis Jaburi, David Johnston +6
Data attribution is a promising field in interpretability that aims to explain model behavior through the influence of its training data, with applications including debugging unde…
When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation
Mubashara Akhtar, Anka Reuel, Prajna Soni +34
Artificial intelligence benchmarks are an important mechanism to measure model progress and guide deployment decisions. However, benchmarks quickly "saturate", making it difficult…
Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations
Anka Reuel, Avijit Ghosh, Jenny Chim +32
Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risks and capabilities. Although general c…
Explaining and Mitigating Crosslingual Tokenizer Inequities
Catherine Arnett, Tyler A. Chang, Stella Biderman +1
The number of tokens it takes to encode parallel text in different languages is known to vary. These disparities are called token premiums. Having high token premiums leads to less…