43 citations · 62 across the 23 of their papers we have counts for
3 papers · 2 filters
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…