9 citations · 16 across the 10 of their papers we have counts for
10 papers
Speculative Interaction Agents: Building Real-Time Agents with Asynchronous I/O and Speculative Tool Calling
Coleman Hooper, Minwoo Kang, Suhong Moon +7
There is a growing demand for agentic AI technologies for a range of downstream applications like customer service and personal assistants. For applications where the agent needs t…
Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim +5
Large language models (LLMs) have shown remarkable advancements in enabling language agents to tackle simple tasks. However, applying them for complex, multi-step, long-horizon tas…
Deep Binding of Language Model Virtual Personas: a Study on Approximating Political Partisan Misperceptions
Minwoo Kang, Suhong Moon, Seung Hyeong Lee +4
Large language models (LLMs) are increasingly capable of simulating human behavior, offering cost-effective ways to estimate user responses to various surveys and polls. However, t…
Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions
Joseph Suh, Erfan Jahanparast, Suhong Moon +2
Large language models (LLMs) present novel opportunities in public opinion research by predicting survey responses in advance during the early stages of survey design. Prior method…
ETS: Efficient Tree Search for Inference-Time Scaling
Coleman Hooper, Sehoon Kim, Suhong Moon +7
Test-time compute scaling has emerged as a new axis along which to improve model accuracy, where additional computation is used at inference time to allow the model to think longer…
TinyAgent: Function Calling at the Edge
Lutfi Eren Erdogan, Nicholas Lee, Siddharth Jha +7
Recent large language models (LLMs) have enabled the development of advanced agentic systems that can integrate various tools and APIs to fulfill user queries through function call…
Efficient and Scalable Estimation of Tool Representations in Vector Space
Suhong Moon, Siddharth Jha, Lutfi Eren Erdogan +4
Recent advancements in function calling and tool use have significantly enhanced the capabilities of large language models (LLMs) by enabling them to interact with external informa…
Virtual Personas for Language Models via an Anthology of Backstories
Suhong Moon, Marwa Abdulhai, Minwoo Kang +5
Large language models (LLMs) are trained from vast repositories of text authored by millions of distinct authors, reflecting an enormous diversity of human traits. While these mode…
An LLM Compiler for Parallel Function Calling
Sehoon Kim, Suhong Moon, Ryan Tabrizi +4
The reasoning capabilities of the recent LLMs enable them to execute external function calls to overcome their inherent limitations, such as knowledge cutoffs, poor arithmetic skil…
Speculative Decoding with Big Little Decoder
Sehoon Kim, Karttikeya Mangalam, Suhong Moon +4
The recent emergence of Large Language Models based on the Transformer architecture has enabled dramatic advancements in the field of Natural Language Processing. However, these mo…