2 citations · 6 across the 17 of their papers we have counts for
7 papers · 1 filter
The State-Prediction Separation Hypothesis
Giovanni Monea, Nathan Godey, Kianté Brantley +1
Transformers use the same forward computation stream to both predict the next token and store useful state for future token predictions. We formulate the \emph{state-prediction sep…
Breadcrumbs Reasoning: Memory-Efficient Reasoning with Compression Beacons
Giovanni Monea, Yair Feldman, Shankar Padmanabhan +2
The scalability of large language models for long-context reasoning is severely constrained by the linear growth of their Transformer key-value cache, which incurs significant memo…
LLMs Are In-Context Bandit Reinforcement Learners
Giovanni Monea, Antoine Bosselut, Kianté Brantley +1
Large Language Models (LLMs) excel at in-context learning (ICL), a supervised learning technique that relies on adding annotated examples to the model context. We investigate a con…
Policy-Gradient Training of Language Models for Ranking
Ge Gao, Jonathan D. Chang, Claire Cardie +2
Text retrieval plays a crucial role in incorporating factual knowledge for decision making into language processing pipelines, ranging from chat-based web search to question answer…
Interactive Text Generation
Felix Faltings, Michel Galley, Baolin Peng +5
Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reflect the interactivity between…
Non-Monotonic Sequential Text Generation
Sean Welleck, Kianté Brantley, Hal Daumé +1
Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a…