Zobrazeno 1 - 10
of 22 653
pro vyhledávání: '"agent approach"'
As modern web services increasingly rely on REST APIs, their thorough testing has become crucial. Furthermore, the advent of REST API specifications such as the OpenAPI Specification has led to the emergence of many black-box REST API testing tools.
Externí odkaz:
http://arxiv.org/abs/2411.07098
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external, domain-specific data into the generative process. While LLMs are highly capable, they often rely on static, pre-trained datasets, limiting their abi
Externí odkaz:
http://arxiv.org/abs/2412.05838
Mitigating Bias in Queer Representation within Large Language Models: A Collaborative Agent Approach
Autor:
Huang, Tianyi, Somasundaram, Arya
Large Language Models (LLMs) often perpetuate biases in pronoun usage, leading to misrepresentation or exclusion of queer individuals. This paper addresses the specific problem of biased pronoun usage in LLM outputs, particularly the inappropriate us
Externí odkaz:
http://arxiv.org/abs/2411.07656
Autor:
Gupta, Aman, Ravichandran, Anirudh, Zhang, Ziji, Shah, Swair, Beniwal, Anurag, Sadagopan, Narayanan
Task-oriented dialogue systems are essential for applications ranging from customer service to personal assistants and are widely used across various industries. However, developing effective multi-domain systems remains a significant challenge due t
Externí odkaz:
http://arxiv.org/abs/2411.00427
Patients with schizophrenia often present with cognitive impairments that may hinder their ability to learn about their condition. These individuals could benefit greatly from education platforms that leverage the adaptability of Large Language Model
Externí odkaz:
http://arxiv.org/abs/2410.12848
In real world software development, improper or missing exception handling can severely impact the robustness and reliability of code. Exception handling mechanisms require developers to detect, capture, and manage exceptions according to high standa
Externí odkaz:
http://arxiv.org/abs/2410.06949
To address the challenge of automating knowledge discovery from a vast volume of literature, in this paper, we introduce a novel framework based on large language models (LLMs) that combines a progressive ontology prompting (POP) algorithm with a dua
Externí odkaz:
http://arxiv.org/abs/2409.00054
Publikováno v:
2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM)
One effective way to optimize the offloading process is by minimizing the transmission time. This is particularly true in a Vehicular Adhoc Network (VANET) where vehicles frequently download and upload High-definition (HD) map data which requires con
Externí odkaz:
http://arxiv.org/abs/2408.03329
Autor:
Stepanova, Alina I.1 (AUTHOR) a.i.stepanova@urfu.ru, Khalyasmaa, Alexandra I.1 (AUTHOR), Matrenin, Pavel V.1 (AUTHOR) p.v.matrenin@urfu.ru, Eroshenko, Stanislav A.1 (AUTHOR)
Publikováno v:
Algorithms. Oct2024, Vol. 17 Issue 10, p447. 25p.
Autor:
Elhenawy, Mohammed, Abutahoun, Ahmad, Alhadidi, Taqwa I., Jaber, Ahmed, Ashqar, Huthaifa I., Jaradat, Shadi, Abdelhay, Ahmed, Glaser, Sebastien, Rakotonirainy, Andry
Multimodal Large Language Models (MLLMs) harness comprehensive knowledge spanning text, images, and audio to adeptly tackle complex problems, including zero-shot in-context learning scenarios. This study explores the ability of MLLMs in visually solv
Externí odkaz:
http://arxiv.org/abs/2407.00092