LLMs | Kiran Tomlinson

AI and the Democratization of Knowledge Work

We survey the literature on AI and knowledge work, focusing on whom the technology benefits. We identify emerging divides in who uses and who benefits from AI’s use in knowledge work.

Madeleine I. G. Daepp, Kiran Tomlinson, Scott Counts, Siddharth Suri

Adopt ≠ Adapt: Longitudinal Analyses of LLM Conversations in the Wild

Although a growing body of research has begun to describe user-LLM interactions, the picture it paints is largely static; little is …

Rebecca M. M. Hicke, Kiran Tomlinson

AI in the Enterprise: How People Use M365 Copilot Chat

M365 Copilot is used every week by millions of people across more than a million companies around the world as part of their workflows. …

Scott Counts, Yan Chen, Jing Dong, Himanshu Sharma, Andrey Zaikin, Rui Hu, Alperen Kok, Gorkem Ozer Yilmaz, Siddharth Suri, Kiran Tomlinson, Sonia Jaffe, Will Wang

Reasoning about Reasoning: BAPO Bounds on Chain-of-Thought Token Complexity in LLMs

Inference-time scaling via chain-of-thought (CoT) reasoning is a major driver of state-of-the-art LLM performance, but it comes with …

Kiran Tomlinson, Tobias Schnabel, Adith Swaminathan, Jennifer Neville

Working with AI: Measuring the Applicability of Generative AI to Occupations

Given the rapid adoption of generative AI and its potential to impact a wide range of tasks, understanding the effects of AI on the …

Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts, Siddharth Suri

Lost in Transmission: When and Why LLMs Fail to Reason Globally

Despite their many successes, transformer-based large language models (LLMs) continue to struggle with tasks that require complex …

Tobias Schnabel, Kiran Tomlinson, Adith Swaminathan, Jennifer Neville