43 citations · 62 across the 23 of their papers we have counts for
13 papers · 1 filter
Matryoshka attribution: Learning to attribute language model outputs to representations and weights
Aryaman Arora, Kirill Acharya, Nathan Hu +4
Attributing language model outputs to their internal computations is an open problem in interpretability. Existing methods, which use causal interventions, gradients, or learnable…
Do Language Models Consistently Encode the Current Year?
Suze van Adrichem, Aditi Bhaskar, Diyi Yang +2
A consistent concept of the current time is important for temporal reasoning, yet how language models represent the current time is not well understood. We contribute two tasks tha…
Outcome Rewards Do Not Guarantee Verifiable or Causally Important Reasoning
Qinan Yu, Alexa Tartaglini, Peter Hase +2
Reinforcement Learning from Verifiable Rewards (RLVR) on chain-of-thought reasoning has become a standard part of language model post-training recipes. A common assumption is that…
HyperSteer: Activation Steering at Scale with Hypernetworks
Jiuding Sun, Sidharth Baskaran, Zhengxuan Wu +3
Steering language models (LMs) by modifying internal activations is a popular approach for controlling text generation. Unsupervised dictionary learning methods, e.g., sparse autoe…
Mechanistic evaluation of Transformers and state space models
Aryaman Arora, Neil Rathi, Nikil Roashan Selvam +3
State space models (SSMs) for language modelling promise an efficient and performant alternative to quadratic-attention Transformers, yet show variable performance on recalling bas…
HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
Jiuding Sun, Jing Huang, Sidharth Baskaran +4
Mechanistic interpretability has made great strides in identifying neural network features (e.g., directions in hidden activation space) that mediate concepts(e.g., the birth year…
AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
Zhengxuan Wu, Aryaman Arora, Atticus Geiger +5
Fine-grained steering of language model outputs is essential for safety and reliability. Prompting and finetuning are widely used to achieve these goals, but interpretability resea…
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…
Demystifying Verbatim Memorization in Large Language Models
Jing Huang, Diyi Yang, Christopher Potts
Large Language Models (LLMs) frequently memorize long sequences verbatim, often with serious legal and privacy implications. Much prior work has studied such verbatim memorization…
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…
Mapping the Increasing Use of LLMs in Scientific Papers
Weixin Liang, Yaohui Zhang, Zhengxuan Wu +11
Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledg…
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).…
RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations
Jing Huang, Zhengxuan Wu, Christopher Potts +2
Individual neurons participate in the representation of multiple high-level concepts. To what extent can different interpretability methods successfully disentangle these roles? To…