7 citations · 8 across the 5 of their papers we have counts for
5 papers
SHRAG: AFrameworkfor Combining Human-Inspired Search with RAG
Hyunseok Ryu, Wonjune Shin, Hyun Park
Retrieval-Augmented Generation (RAG) is gaining recognition as one of the key technological axes for next generation information retrieval, owing to its ability to mitigate the hal…
Motif 2.6B Technical Report
Junghwan Lim, Sungmin Lee, Dongseok Kim +22
Recent advancements in Large Language Models (LLMs) have revolutionized artificial intelligence, yet developing an effective foundational LLM that balances high performance with co…
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
Gary Tom, Cher Tian Ser, Ella M. Rajaonson +4
Olfaction -- how molecules are perceived as odors to humans -- remains poorly understood. Recently, the principal odor map (POM) was introduced to digitize the olfactory properties…
Dataverse: Open-Source ETL (Extract, Transform, Load) Pipeline for Large Language Models
Hyunbyung Park, Sukyung Lee, Gyoungjin Gim +3
To address the challenges associated with data processing at scale, we propose Dataverse, a unified open-source Extract-Transform-Load (ETL) pipeline for large language models (LLM…
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
Dahyun Kim, Chanjun Park, Sanghoon Kim +15
We introduce SOLAR 10.7B, a large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. Inspired…