activity
20172026
most citedLlama 2: Open Foundation and Fine-Tuned Chat Models

2.7k citations · 2.7k across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2026

KellyBench: A Benchmark for Long-Horizon Sequential Decision Making

Thomas Grady, Kip Parker, Iliyan Zarov +3

Language models are saturating benchmarks for procedural tasks with narrow objectives. But they are increasingly being deployed in long-horizon, non-stationary environments with op…

cs.SE2026★ 1 cited

The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes

Redacted by arXiv

This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…

cs.AI2024

The Llama 3 Herd of Models

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…

cs.CL2024★ 1 cited

"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing

Vipula Rawte, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman +4

Hallucination has emerged as the most vulnerable aspect of contemporary Large Language Models (LLMs). In this paper, we introduce the Sorry, Come Again (SCA) prompting, aimed to av…

cs.CL2023★ 2.7k cited

Llama 2: Open Foundation and Fine-Tuned Chat Models

Hugo Touvron, Louis Martin, Kevin Stone +65

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our f…

cs.LG2017★ 12 cited

A Universal Marginalizer for Amortized Inference in Generative Models

Laura Douglas, Iliyan Zarov, Konstantinos Gourgoulias +6

We consider the problem of inference in a causal generative model where the set of available observations differs between data instances. We show how combining samples drawn from t…