Karthik Narasimhan

PruMUX: Augmenting Data Multiplexing with Model Compression

2023

As language models increase in size by the day, methods for efficient inference are critical to leveraging their capabilities for various applications. Prior work has investigated techniques like model pruning, knowledge distillation, and data multiplexing …

FireAct: Toward Language Agent Fine-tuning

2023 · arXiv (Cornell University)

Recent efforts have augmented language models (LMs) with external tools or environments, leading to the development of language agents that can reason and act. However, most of these agents rely on few-shot prompting techniques with …

Distributing Accountability, Not Capability: Phase Separation and the LLM Workflow Quadrant in Autonomous AI Agent Architectures

2022 · arXiv (Cornell University)

Autonomous AI agents in business deployments exhibit a recurring failure mode: when an incident occurs, responsibility cannot be redirected to a separable contributor. The dominant discourse treats this as a single phenomenon, addressed by sandboxing, …

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

2023 · arXiv (Cornell University)

Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks …