1 citations · 1 across the 2 of their papers we have counts for
6 papers
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…
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…
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…
A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs
Trenton Chang, Tobias Schnabel, Adith Swaminathan +1
Despite advances in large language models (LLMs) on reasoning and instruction-following tasks, it is unclear whether they can reliably produce outputs aligned with a variety of use…
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…
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…