24 citations · 53 across the 13 of their papers we have counts for
5 papers · 1 filter
Enhancing LLM Planning Capabilities through Intrinsic Self-Critique
Bernd Bohnet, Pierre-Alexandre Kamienny, Hanie Sedghi +7
We demonstrate an approach for LLMs to critique their \emph{own} answers with the goal of enhancing their performance that leads to significant improvements over established planni…
Many-Shot In-Context Learning
Rishabh Agarwal, Avi Singh, Lei M. Zhang +12
Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expande…
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
Avi Singh, John D. Co-Reyes, Rishabh Agarwal +38
Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of hi…
Gradient-Free Structured Pruning with Unlabeled Data
Azade Nova, Hanjun Dai, Dale Schuurmans
Large Language Models (LLMs) have achieved great success in solving difficult tasks across many domains, but such success comes with a high computation cost, and inference latency.…
Teaching Algorithmic Reasoning via In-context Learning
Hattie Zhou, Azade Nova, Hugo Larochelle +3
Large language models (LLMs) have shown increasing in-context learning capabilities through scaling up model and data size. Despite this progress, LLMs are still unable to solve al…