4 citations · 7 across the 4 of their papers we have counts for
4 papers
Reaching Beyond the Mode: RL for Distributional Reasoning in Language Models
Isha Puri, Mehul Damani, Idan Shenfeld +3
Given a question, a language model (LM) implicitly encodes a distribution over possible answers. In practice, post-training procedures for LMs often collapse this distribution onto…
Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty
Mehul Damani, Isha Puri, Stewart Slocum +4
When language models (LMs) are trained via reinforcement learning (RL) to generate natural language "reasoning chains", their performance improves on a variety of difficult questio…
The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
Ekin Akyürek, Mehul Damani, Adam Zweiger +5
Language models (LMs) have shown impressive performance on tasks within their training distribution, but often struggle with structurally novel tasks even when given a small number…
In-Context Language Learning: Architectures and Algorithms
Ekin Akyürek, Bailin Wang, Yoon Kim +1
Large-scale neural language models exhibit a remarkable capacity for in-context learning (ICL): they can infer novel functions from datasets provided as input. Most of our current…