Generative AI (GenAI) represented by large language models (LLMs) is revolutionizing the way we approach problem-solving and content creation in various domains. With its ability to interact with human in natural language, as well as its enormous knowledge base trained from gigantic volume of data, it holds immense potential for enterprise applications. In fact, almost all major companies now have dedicated teams focusing on developing GenAI solutions to better serve their customers and for internal applications.
However, the unique characteristics of enterprise data, such as its specificity, structure, and dynamic nature, pose distinct challenges. Retrieval-Augmented Generation (RAG) systems, a subset of generative AI, offer a promising solution by combining information retrieval with text generation to create rich, contextually relevant responses. These systems can harness enterprise data to provide tailored solutions, addressing the challenges of scalability, security, and data quality. Moreover, enterprise applications pose additional challenges such as scalability, security, reliability, and compliance
to GenAI solutions.
This workshop aims to bring together academic researchers and industrial practitioners who are interested in building GenAI solutions for enterprise AI, with a special focus on RAG systems. The workshop will provide a platform for sharing the latest research advances, practical experiences, and real-world challenges in this emerging field. The workshop will also foster collaboration and cross-disciplinary discussion among the participants, and identify future directions and opportunities.
We seek contributions on all aspects related to GenAI usages in Enterprise AI.
The topics of interest include, but are not limited to:
Enterprise AI dataset and collection techniques
RAG systems for specific enterprise domains or tasks
Retrieval strategies and models for RAG systems
Text-to-SQL design and solutions
Foundation model pre-training, prompting and fine-tuning
Hallucination prevention
Multi-modal and multi-lingual
RAG systems
Evaluation metrics
Relevance and diversity metrics for evaluation for RAG systems
Scalability and efficiency issues and solutions for RAG systems
Security and privacy aspects and solutions for RAG systems
Compliance and ethical issues and solutions for RAG systems
User feedback and interaction mechanisms for RAG systems
Case studies and best practices
All submissions must be PDFs formatted in the Standard ACM Conference Proceedings Template. Submitted papers will be assessed based on their quality, impact, novelty, depth, clarity, and generalizability. For each accepted paper, at least one author must attend the workshop and present the paper or poster.
All accepted papers will be presented as posters and some would be selected for oral presentations, depending on schedule constraints. Accepted papers will be posted on the workshop website and also will be eligible to be published in the ACM Digital Library. Papers can be up to 6 pages long.
We accept two types of paper submissions:
Track 1: Full length papers.
Submissions in this track can be up to 6 pages, plus any pages of additional references and appendices.
Authors should be noted that reviewers are not required to read appendices.
Track 2: Extended abstract.
Submission for this track can be up to 2 pages, plus any pages of additional references and appendices. Authors should be noted that reviewers are not required to read appendices.
For this track, we encourage submissions that describe visionary ideas, preliminary results, controversial findings, or experience sharing. Acceptable material includes work that has already been submitted or published. Authors are responsible for ensuring compliance with the policies of other venues.
Reviews are single-blind. Please include author names and affiliations in your submission.
Link to submission site here [https://easychair.org/conferences/?conf=ragenterprise24]
Submission deadline: August 15, 2024 August 24, 2024
Author notification : August 30, 2024 September 3, 2024