Feb. 28, 2024, 5:49 a.m. | Gurusha Juneja, Subhabrata Dutta, Soumen Chakrabarti, Sunny Manchanda, Tanmoy Chakraborty

cs.CL updates on arXiv.org arxiv.org

arXiv:2310.18338v2 Announce Type: replace
Abstract: Large Language Models (LLMs) prompted to generate chain-of-thought (CoT) exhibit impressive reasoning capabilities. Recent attempts at prompt decomposition toward solving complex, multi-step reasoning problems depend on the ability of the LLM to simultaneously decompose and solve the problem. A significant disadvantage is that foundational LLMs are typically not available for fine-tuning, making adaptation computationally prohibitive. We believe (and demonstrate) that problem decomposition and solution generation are distinct capabilites, better addressed in separate modules, than by …

abstract arxiv capabilities cs.ai cs.cl generate language language models large language large language models llm llms prompt reasoning small small language models solve thought type

Data Scientist (m/f/x/d)

@ Symanto Research GmbH & Co. KG | Spain, Germany

Aumni - Site Reliability Engineer III - MLOPS

@ JPMorgan Chase & Co. | Salt Lake City, UT, United States

Senior Data Analyst

@ Teya | Budapest, Hungary

Technical Analyst (Data Analytics)

@ Contact Government Services | Chicago, IL

Engineer, AI/Machine Learning

@ Masimo | Irvine, CA, United States

Private Bank - Executive Director: Data Science and Client / Business Intelligence

@ JPMorgan Chase & Co. | Mumbai, Maharashtra, India