1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CL2026
Spontaneous Context Restoration: How Language Models Recover from Corrupted Inputs
Pranjal Garg, Jacob Beck
Language models sometimes produce correct outputs even when their inputs are corrupted by deletion, replacement, or misspelling. We study the internal processes accompanying this b…
cs.LG2026
Hill Sampling for Test-Time Scaling: A Simple and Better Alternative to Repeated Sampling, Evolution, and Training
Jacob Beck, Philip V. Ogren, Ari Kobren
Large language models (LLMs) can improve solutions to verifiable scientific and algorithmic problems by spending additional computation at test time. Recent systems achieve strong…
cs.SE2024★ 1 cited
Cracking the Code: Evaluating Zero-Shot Prompting Methods for Providing Programming Feedback
Niklas Ippisch, Anna-Carolina Haensch, Jan Simson +3
Despite the growing use of large language models (LLMs) for providing feedback, limited research has explored how to achieve high-quality feedback. This case study introduces an ev…