2 citations · 3 across the 10 of their papers we have counts for
4 papers · 1 filter
Transformers are Minimax Optimal Nonparametric In-Context Learners
Juno Kim, Tai Nakamaki, Taiji Suzuki
In-context learning (ICL) of large language models has proven to be a surprisingly effective method of learning a new task from only a few demonstrative examples. In this paper, we…
Transformers Provably Solve Parity Efficiently with Chain of Thought
Juno Kim, Taiji Suzuki
This work provides the first theoretical analysis of training transformers to solve complex problems by recursively generating intermediate states, analogous to fine-tuning for cha…
A Novel Nuanced Conversation Evaluation Framework for Large Language Models in Mental Health
Alexander Marrapese, Basem Suleiman, Imdad Ullah +1
Understanding the conversation abilities of Large Language Models (LLMs) can help lead to its more cautious and appropriate deployment. This is especially important for safety-crit…
Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape
Juno Kim, Taiji Suzuki
Large language models based on the Transformer architecture have demonstrated impressive capabilities to learn in context. However, existing theoretical studies on how this phenome…