1 citations · 1 across the 2 of their papers we have counts for
5 papers
LLMs Corrupt Your Documents When You Delegate
Philippe Laban, Tobias Schnabel, Jennifer Neville
Large Language Models (LLMs) are poised to disrupt knowledge work, with the emergence of delegated work as a new interaction paradigm (e.g., vibe coding). Delegation requires trust…
Reasoning about Reasoning: BAPO Bounds on Chain-of-Thought Token Complexity in LLMs
Kiran Tomlinson, Tobias Schnabel, Adith Swaminathan +1
Inference-time scaling via chain-of-thought (CoT) reasoning is a major driver of state-of-the-art LLM performance, but it comes with substantial latency and compute costs. We addre…
Lost in Transmission: When and Why LLMs Fail to Reason Globally
Tobias Schnabel, Kiran Tomlinson, Adith Swaminathan +1
Despite their many successes, transformer-based large language models (LLMs) continue to struggle with tasks that require complex reasoning over large parts of their input. We argu…
On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots
Christine Herlihy, Jennifer Neville, Tobias Schnabel +1
We explore the use of Large Language Model (LLM-based) chatbots to power recommender systems. We observe that the chatbots respond poorly when they encounter under-specified reques…
Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization
Tobias Schnabel, Jennifer Neville
In many modern LLM applications, such as retrieval augmented generation, prompts have become programs themselves. In these settings, prompt programs are repeatedly called with diff…