Aligning (Medical) LLMs for (Counterfactual) Fairness

R Poulain, H Fayyaz, R Beheshti - arxiv preprint arxiv:2408.12055, 2024 - arxiv.org
Large Language Models (LLMs) have emerged as promising solutions for a variety of
medical and clinical decision support applications. However, LLMs are often subject to …

Large Concept Models: Language Modeling in a Sentence Representation Space

LCM The, L Barrault, PA Duquenne, M Elbayad… - arxiv preprint arxiv …, 2024 - arxiv.org
LLMs have revolutionized the field of artificial intelligence and have emerged as the de-facto
tool for many tasks. The current established technology of LLMs is to process input and …

Training Sparse Mixture Of Experts Text Embedding Models

Z Nussbaum, B Duderstadt - arxiv preprint arxiv:2502.07972, 2025 - arxiv.org
Transformer-based text embedding models have improved their performance on
benchmarks like MIRACL and BEIR by increasing their parameter counts. However, this …

Using Embeddings to Improve Named Entity Recognition Classification with Graphs

G Silva, M Rodrigues, A Teixeira… - 13th Symposium on …, 2024 - drops.dagstuhl.de
Richer information has potential to improve performance of NLP (Natural Language
Processing) tasks such as Named Entity Recognition. A linear sequence of words can be …

LLM-Cite: Cheap Fact Verification with Attribution via URL Generation

N Joshi, A Taly, D Muppalla - openreview.net
Hallucinations are one of the main issues with Large Language Models (LLMs). This has led
to increased interest in automated ways to verify the factuality of LLMs' responses. Existing …