4 citations · 5 across the 9 of their papers we have counts for
15 papers · 1 filter
Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +571
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 t…
The Rarity Blind Spot: A Framework for Evaluating Statistical Reasoning in LLMs
Seiji Maekawa, Hayate Iso, Nikita Bhutani
Effective decision-making often relies on identifying what makes each candidate distinctive. While existing benchmarks for LLMs emphasize retrieving or summarizing information rele…
Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education
Hayate Iso, Pouya Pezeshkpour, Nikita Bhutani +1
Large Language Models (LLMs) offer the potential to automate hiring by matching job descriptions with candidate resumes, streamlining recruitment processes, and reducing operationa…
From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization
Catarina G. Belem, Pouya Pezeshkpour, Hayate Iso +3
Although many studies have investigated and reduced hallucinations in large language models (LLMs) for single-document tasks, research on hallucination in multi-document summarizat…
Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data
Seiji Maekawa, Hayate Iso, Nikita Bhutani
The rapid increase in textual information means we need more efficient methods to sift through, organize, and understand it all. While retrieval-augmented generation (RAG) models e…
Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models
Seiji Maekawa, Hayate Iso, Sairam Gurajada +1
While large language models (LMs) demonstrate remarkable performance, they encounter challenges in providing accurate responses when queried for information beyond their pre-traine…