8 citations · 18 across the 14 of their papers we have counts for
16 papers
Amortized Off-Policy Evaluation for LLMs
Younwoo Choi, Leo Feng, Vincent Liu +1
Accurate evaluation is central to selecting which LLM to deploy, yet testing a candidate on live traffic exposes real users to an unvetted model. Teams therefore evaluate candidate…
Dynamic Rebatching for Efficient Early-Exit Inference with DREX
Xuting Liu, Daniel Alexander, Siva Kesava Reddy Kakarla +2
Early-Exit (EE) is a Large Language Model (LLM) architecture that accelerates inference by allowing easier tokens to be generated using only a subset of the model's layers. However…
Context-Action Embedding Learning for Off-Policy Evaluation in Contextual Bandits
Kushagra Chandak, Vincent Liu, Haanvid Lee
We consider off-policy evaluation (OPE) in contextual bandits with finite action space. Inverse Propensity Score (IPS) weighting is a widely used method for OPE due to its unbiased…
The Dawn of Disaggregation and the Coherence Conundrum: A Call for Federated Coherence
Jaewan Hong, Marcos K. Aguilera, Emmanuel Amaro +3
Disaggregated memory is an upcoming data center technology that will allow nodes (servers) to share data efficiently. Sharing data creates a debate on the level of cache coherence…
Switching the Loss Reduces the Cost in Batch (Offline) Reinforcement Learning
Alex Ayoub, Kaiwen Wang, Vincent Liu +5
We propose training fitted Q-iteration with log-loss (FQI-log) for batch reinforcement learning (RL). We show that the number of samples needed to learn a near-optimal policy with…
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
Debarati Das, Karin De Langis, Anna Martin-Boyle +14
This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including ann…