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20192024
most citedLLaMA: Open and Efficient Foundation Language Models

4k citations · 8.4k across the 17 of their papers we have counts for

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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.CL2024★ 2 cited

When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay +9

Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are take…

cs.CL2023★ 425 cited

Code Llama: Open Foundation Models for Code

Baptiste Rozière, Jonas Gehring, Fabian Gloeckle +23

We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for lar…

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.CL2023★ 4k cited

LLaMA: Open and Efficient Foundation Language Models

Hugo Touvron, Thibaut Lavril, Gautier Izacard +11

We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train…