121 citations · 591 across the 29 of their papers we have counts for
5 papers · 2 filters
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Ling Yang, Zhaochen Yu, Tianjun Zhang +5
We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs).…
GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Applications
Shishir G. Patil, Tianjun Zhang, Vivian Fang +7
Large Language Models (LLMs) are evolving beyond their classical role of providing information within dialogue systems to actively engaging with tools and performing actions on rea…
LLoCO: Learning Long Contexts Offline
Sijun Tan, Xiuyu Li, Shishir Patil +5
Processing long contexts remains a challenge for large language models (LLMs) due to the quadratic computational and memory overhead of the self-attention mechanism and the substan…
RAFT: Adapting Language Model to Domain Specific RAG
Tianjun Zhang, Shishir G. Patil, Naman Jain +4
Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additi…
In-Context Principle Learning from Mistakes
Tianjun Zhang, Aman Madaan, Luyu Gao +5
In-context learning (ICL, also known as few-shot prompting) has been the standard method of adapting LLMs to downstream tasks, by learning from a few input-output examples. Nonethe…