2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CL2025
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…
cs.CL2025
Leveraging Large Language Models for Zero-shot Lay Summarisation in Biomedicine and Beyond
Tomas Goldsack, Carolina Scarton, Chenghua Lin
In this work, we explore the application of Large Language Models to zero-shot Lay Summarisation. We propose a novel two-stage framework for Lay Summarisation based on real-life pr…
cs.CL2024★ 2 cited
From Facts to Insights: A Study on the Generation and Evaluation of Analytical Reports for Deciphering Earnings Calls
Tomas Goldsack, Yang Wang, Chenghua Lin +1
This paper explores the use of Large Language Models (LLMs) in the generation and evaluation of analytical reports derived from Earnings Calls (ECs). Addressing a current gap in re…