Search Results for author:
Found 5 papers, 1 papers with code
Psychological Assessments with Large Language Models: A Privacy-Focused and Cost-Effective Approach
This study explores the use of Large Language Models (LLMs) to analyze text comments from Reddit users, aiming to achieve two primary objectives: firstly, to pinpoint critical excerpts that support a predefined psychological assessment of suicidal risk; and secondly, to summarize the material to substantiate the preassigned suicidal risk level.
Experimenting with Large Language Models and vector embeddings in NASA SciX
Open-source Large Language Models enable projects such as NASA SciX (i. e., NASA ADS) to think out of the box and try alternative approaches for information retrieval and data augmentation, while respecting data copyright and users' privacy.
Improving astroBERT using Semantic Textual Similarity
The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing.
Building astroBERT, a language model for Astronomy & Astrophysics
The existing search tools for exploring the NASA Astrophysics Data System (ADS) can be quite rich and empowering (e. g., similar and trending operators), but researchers are not yet allowed to fully leverage semantic search.
Determination of spectroscopic parameters for 313 M dwarf stars from their APOGEE Data Release 16 H-band spectra
The scientific community's interest on the stellar parameters of M dwarfs has been increasing over the last few years, with potential applications ranging from galactic characterization to exoplanet detection.
Solar and Stellar Astrophysics
