1 citations · 1 across the 3 of their papers we have counts for
5 papers
Understanding and Mitigating Role Spoofing in LLMs
Ezra Edelman, Benjamin Edelman, Surbhi Goel +1
Despite growing capabilities, LLMs remain susceptible to prompt injections: malicious inputs that can manipulate or hijack models. In particular, it has recently been observed that…
Less Data, Faster Training: repeating smaller datasets speeds up learning via sampling biases
Jingwen Liu, Ezra Edelman, Surbhi Goel +1
This work investigates the ``small-vs-large gap'', where repeating on fewer samples can lead to compute saving during training compared to using a larger dataset. This is observed…
Reliable Abstention under Adversarial Injections: Tight Lower Bounds and New Upper Bounds
Ezra Edelman, Surbhi Goel
We study online learning in the adversarial injection model introduced by [Goel et al. 2017], where a stream of labeled examples is predominantly drawn i.i.d.\ from an unknown dist…
Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones
Parsa Mirtaheri, Ezra Edelman, Samy Jelassi +2
Inference-time computation has emerged as a promising scaling axis for improving large language model reasoning. However, despite yielding impressive performance, the optimal alloc…
The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains
Benjamin L. Edelman, Ezra Edelman, Surbhi Goel +2
Large language models have the ability to generate text that mimics patterns in their inputs. We introduce a simple Markov Chain sequence modeling task in order to study how this i…