東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Advances in explainability, agents, and large language models = first International Workshop on Causality, Agents and Large Models, CALM 2024, Kyoto, Japan, November 18-19, 2024 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advances in explainability, agents, and large language models/ edited by Yazan Mualla ... [et al.].
Reminder of title:	first International Workshop on Causality, Agents and Large Models, CALM 2024, Kyoto, Japan, November 18-19, 2024 : proceedings /
remainder title:	CALM 2024
other author:	Mualla, Yazan.
corporate name:	CALM (Workshop)
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	ix, 127 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Enhancing Personalized Nutrition: A Hybrid Intelligence Approach with LLM-Powered Meal Planning. -- Generating Explanations for Molecular Property Predictions in Graph Neural Networks. -- Balancing (Normative) Reasons for the Intelligent Human-input-based Blockchain Oracle. -- Feature Generation Using LLMs: An Evolutionary Algorithm Approach. -- Augmenting Dark Patterns Text Data by Leveraging Large Language Models: a Multi-Agent Framework and Parameter-Efficient Fine-Tuning. -- Assessing the Robustness of LLMs in Predicting Supports and Attacks. -- Enhancing accuracy and explainability in anomaly classification with large language models. -- Agent-Based Hate Speech Moderation Approach.
Contained By:	Springer Nature eBook
Subject:	Intelligent agents (Computer software) - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-031-89103-8
ISBN:	9783031891038

Advances in explainability, agents, and large language models = first International Workshop on Causality, Agents and Large Models, CALM 2024, Kyoto, Japan, November 18-19, 2024 : proceedings /
Advances in explainability, agents, and large language modelsfirst International Workshop on Causality, Agents and Large Models, CALM 2024, Kyoto, Japan, November 18-19, 2024 : proceedings /[electronic resource] :CALM 2024edited by Yazan Mualla ... [et al.]. - Cham :Springer Nature Switzerland :2025. - ix, 127 p. :ill. (some col.), digital ;24 cm. - Communications in computer and information science,24711865-0937 ;. - Communications in computer and information science ;2471..

Enhancing Personalized Nutrition: A Hybrid Intelligence Approach with LLM-Powered Meal Planning. -- Generating Explanations for Molecular Property Predictions in Graph Neural Networks. -- Balancing (Normative) Reasons for the Intelligent Human-input-based Blockchain Oracle. -- Feature Generation Using LLMs: An Evolutionary Algorithm Approach. -- Augmenting Dark Patterns Text Data by Leveraging Large Language Models: a Multi-Agent Framework and Parameter-Efficient Fine-Tuning. -- Assessing the Robustness of LLMs in Predicting Supports and Attacks. -- Enhancing accuracy and explainability in anomaly classification with large language models. -- Agent-Based Hate Speech Moderation Approach.

This book constitutes the refereed proceedings of the First International Workshop on Advances in explainability, agents, and large language models, CALM 2024, held in Kyoto, Japan, during November 18-19, 2024. The 7 full papers and 1 short paper presented in this book were carefully reviewed and selected from 17 submissions. The Workshop on Causality, Agents, and Large Models (CALM) was established to foster interdisciplinary collaboration and advance research at the intersection of causal reasoning, multi-agent systems (MAS), and large language models (LLMs).

ISBN: 9783031891038

Standard No.: 10.1007/978-3-031-89103-8doiSubjects--Topical Terms:

582135
Intelligent agents (Computer software)
--Congresses.

LC Class. No.: QA76.76.I58

Dewey Class. No.: 006.30285436

Advances in explainability, agents, and large language models = first International Workshop on Causality, Agents and Large Models, CALM 2024, Kyoto, Japan, November 18-19, 2024 : proceedings /
LDR:02515nmm a2200349 a 4500 001 2409705
003 DE-He213
005 20250424124647.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031891038 $q (electronic bk.)
020 $a 9783031891021 $q (paper)
024 7 $a 10.1007/978-3-031-89103-8 $2 doi
035 $a 978-3-031-89103-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.76.I58
072 7 $a UY $2 bicssc
072 7 $a COM000000 $2 bisacsh
072 7 $a UY $2 thema
082 0 4 $a 006.30285436 $2 23
090 $a QA76.76.I58 $b C164 2024
111 2 $a CALM (Workshop) $n (1st : $d 2024 : $c Kyoto, Japan) $3 3783056
245 1 0 $a Advances in explainability, agents, and large language models $h [electronic resource] : $b first International Workshop on Causality, Agents and Large Models, CALM 2024, Kyoto, Japan, November 18-19, 2024 : proceedings / $c edited by Yazan Mualla ... [et al.].
246 3 $a CALM 2024
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a ix, 127 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Communications in computer and information science, $x 1865-0937 ; $v 2471
505 0 $a Enhancing Personalized Nutrition: A Hybrid Intelligence Approach with LLM-Powered Meal Planning. -- Generating Explanations for Molecular Property Predictions in Graph Neural Networks. -- Balancing (Normative) Reasons for the Intelligent Human-input-based Blockchain Oracle. -- Feature Generation Using LLMs: An Evolutionary Algorithm Approach. -- Augmenting Dark Patterns Text Data by Leveraging Large Language Models: a Multi-Agent Framework and Parameter-Efficient Fine-Tuning. -- Assessing the Robustness of LLMs in Predicting Supports and Attacks. -- Enhancing accuracy and explainability in anomaly classification with large language models. -- Agent-Based Hate Speech Moderation Approach.
520 $a This book constitutes the refereed proceedings of the First International Workshop on Advances in explainability, agents, and large language models, CALM 2024, held in Kyoto, Japan, during November 18-19, 2024. The 7 full papers and 1 short paper presented in this book were carefully reviewed and selected from 17 submissions. The Workshop on Causality, Agents, and Large Models (CALM) was established to foster interdisciplinary collaboration and advance research at the intersection of causal reasoning, multi-agent systems (MAS), and large language models (LLMs).
650 0 $a Intelligent agents (Computer software) $x Congresses. $3 582135
650 1 4 $a Computer Science. $3 626642
700 1 $a Mualla, Yazan. $3 3783057
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Communications in computer and information science ; $v 2471. $3 3783058
856 4 0 $u https://doi.org/10.1007/978-3-031-89103-8
950 $a Computer Science (SpringerNature-11645)