37 citations · 63 across the 28 of their papers we have counts for
6 papers · 1 filter
Bayesian scaling laws for in-context learning
Aryaman Arora, Dan Jurafsky, Christopher Potts +1
In-context learning (ICL) is a powerful technique for getting language models to perform complex tasks with no training updates. Prior work has established strong correlations betw…
ReFT: Representation Finetuning for Language Models
Zhengxuan Wu, Aryaman Arora, Zheng Wang +4
Parameter-efficient finetuning (PEFT) methods seek to adapt large neural models via updates to a small number of weights. However, much prior interpretability work has shown that r…
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Zhengxuan Wu, Atticus Geiger, Aryaman Arora +5
Interventions on model-internal states are fundamental operations in many areas of AI, including model editing, steering, robustness, and interpretability. To facilitate such resea…
CausalGym: Benchmarking causal interpretability methods on linguistic tasks
Aryaman Arora, Dan Jurafsky, Christopher Potts
Language models (LMs) have proven to be powerful tools for psycholinguistic research, but most prior work has focused on purely behavioural measures (e.g., surprisal comparisons).…
Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens
Nay San, Georgios Paraskevopoulos, Aryaman Arora +4
While massively multilingual speech models like wav2vec 2.0 XLSR-128 can be directly fine-tuned for automatic speech recognition (ASR), downstream performance can still be relative…
A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments
Zhengxuan Wu, Atticus Geiger, Jing Huang +4
We respond to the recent paper by Makelov et al. (2023), which reviews subspace interchange intervention methods like distributed alignment search (DAS; Geiger et al. 2023) and cla…