7 papers
Analysis of Optimality of Large Language Models on Planning Problems
Bernd Bohnet, Michael C. Mozer, Kevin Swersky +4
Classic AI planning problems have been revisited in the Large Language Model (LLM) era, with a focus of recent benchmarks on success rates rather than plan efficiency. We examine t…
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…
A Comparative Analysis of LLM Adaptation: SFT, LoRA, and ICL in Data-Scarce Scenarios
Bernd Bohnet, Rumen Dangovski, Kevin Swersky +4
The remarkable capabilities of Large Language Models (LLMs) often need to be tailored for specific applications, requiring the integration of new knowledge or the acquisition of ne…
Improving Large Language Model Planning with Action Sequence Similarity
Xinran Zhao, Hanie Sedghi, Bernd Bohnet +2
Planning is essential for artificial intelligence systems to look ahead and proactively determine a course of actions to reach objectives in the virtual and real world. Recent work…
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…
Exploring and Benchmarking the Planning Capabilities of Large Language Models
Bernd Bohnet, Azade Nova, Aaron T Parisi +6
Classical and natural language planning tasks remain a difficult domain for modern large language models (LLMs). In this work, we lay the foundations for improving planning capabil…