Large language models are increasingly becoming a cornerstone technology in artificial
intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset …

Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating
up-to-date information, mitigating hallucinations, and enhancing response quality …

Large language model (LLM) applications in cloud root cause analysis (RCA) have been
actively explored recently. However, current methods are still reliant on manual workflow …

Rapid growth in the gig economy has been facilitated by the increased use of algorithmic
management (AM) in online platforms (OPs) coordinating gig work. There has been a …

Existing retrieval benchmarks primarily consist of information-seeking queries (eg,
aggregated questions from search engines) where keyword or semantic-based retrieval is …

We present Gecko, a compact and versatile text embedding model. Gecko achieves strong
retrieval performance by leveraging a key idea: distilling knowledge from large language …

Large language models (LLMs) with decoder-only architectures demonstrate remarkable in-
context learning (ICL) capabilities. This feature enables them to effectively handle both …

This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for
Ontology Learning Challenge. LLMs4OL is a community development initiative collocated …

Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations
such as hallucinations and outdated knowledge. Knowledge Graph (KG)-based Retrieval …

We propose the problem of conversational web navigation, where a digital agent controls a
web browser and follows user instructions to solve real-world tasks in a multi-turn dialogue …