Feb. 6, 2024, 5:53 a.m. | Palaash Agrawal Shavak Vasania Cheston Tan

cs.CL updates on arXiv.org arxiv.org

Pretrained Large Language Models have demonstrated various types of reasoning capabilities through language-based prompts alone. However, in this paper, we test the depth of graph reasoning for 5 different LLMs (GPT-4, GPT-3.5, Claude-2, Llama-2 and Palm-2) through the problems of graph reasoning. In particular, we design 10 distinct problems of graph traversal, each representing increasing levels of complexity. Further, we analyze the performance of models across various settings such as varying sizes of graphs as well as different forms of …

capabilities claude cs.ai cs.cl design gpt gpt-3 gpt-3.5 gpt-4 graph language language models large language large language models limitations llama llms palm paper prompts reasoning test through types

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Business Data Analyst

@ Alstom | Johannesburg, GT, ZA