Feb. 13, 2024, 5:42 a.m. | Zhen-Yu Zhang Siwei Han Huaxiu Yao Gang Niu Masashi Sugiyama

cs.LG updates on arXiv.org arxiv.org

To improve the ability of the large language model (LLMs) to handle complex reasoning problems, chain-of-thoughts (CoT) methods were proposed to guide LLMs to reason step-by-step, facilitating problem solving from simple to complex tasks. State-of-the-art approaches for generating such a chain involve interactive collaboration, where the learner generates candidate intermediate thoughts, evaluated by the LLM, guiding the generation of subsequent thoughts. However, a widespread yet understudied problem is that the evaluation from the LLM is typically noisy and unreliable, potentially …

art collaboration comparison cs.ai cs.cl cs.lg guide interactive intermediate language language model large language large language model llms reason reasoning searching simple state step-by-step tasks thought thoughts

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Data Architect

@ Dyson | India - Bengaluru IT Capability Centre

GTM Operation and Marketing Data Analyst

@ DataVisor | Toronto, Ontario, Canada - Remote

Associate - Strategy & Business Intelligence

@ Hitachi | (HE)Office Rotterdam

Senior Executive - Data Analysis

@ Publicis Groupe | Beirut, Lebanon