SICSA REALLM workshop 2024: proceedings of the SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024)

Abstract

In the last few years, Large Language Models (LLMs) underpinned by rapid advancements in Artificial Intelligence (AI) have grabbed the attention of academia, industry and the general public. Demonstrating hence before unseen capability to deal with a wide-range of tasks across data modalities, LLMs have opened research avenues across the broad domains of knowledge-management tasks. Equally however, these advancements create new opportunities and challenges, both in the design of LLMs and in their practical application. Among these is the challenge of integrating reasoning within LLM architectures, to prevent repetition of mistakes or spreading of disinformation. In such circumstances, explainability and traceability of model outcomes becomes paramount, particularly when these algorithms present opportunity for optimisation within safety-critical domains, such as healthcare. Understanding the limits of LLM performance in such domains is paramount, with a need for the identification of robust evaluation measures to ensure that these algorithms are working safely and effectively in situ.

Citation

MARTIN, K., SALIMI, P. and WIJAYASEKARA, V. (eds.) 2024. SICSA REALLM workshop 2024: proceedings of the 2024 SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024), 17 October 2024, Aberdeen, UK. CEUR workshop proceedings, 3822. Aachen: CEUR-WS [online]. Available from: https://ceur-ws.org/Vol-3822/

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2024 SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024)
• Start Date: Nov 17, 2024
• Acceptance Date: Oct 1, 2024
• Online Publication Date: Oct 29, 2024
• Publication Date: Nov 11, 2024
• Deposit Date: Dec 3, 2024
• Publicly Available Date: Dec 3, 2024
• Publisher: CEUR-WS
• Peer Reviewed: Peer Reviewed
• Series Title: CEUR-workshop proceedings
• Series Number: 3822
• Series ISSN: 1613-0073
• Keywords: Large language models (LLMs); Artificial Intelligence (AI)
• Public URL: https://rgu-repository.worktribe.com/output/2613264
• Publisher URL: https://ceur-ws.org/Vol-3822
• Additional Information: This file contains the preface of the proceedings. The full set of proceedings are available from the publisher’s website: https://ceur-ws.org/Vol-3822/