6 papers
Propose and Attend: Training-free MLLM Grounding Confidence via Multi-Token Localized Attention
Daniel Shalam, Emanuel Ben Baruch, Avi Ben Cohen +1
Multimodal large language models can emit localized predictions, bounding boxes for objects and temporal windows for video and audio events, but they hallucinate these regions prol…
Linguistically Informed Evaluation of Multilingual ASR for African Languages
Fei-Yueh Chen, Lateef Adeleke, C. M. Downey
Word Error Rate (WER) mischaracterizes ASR models' performance for African languages by combining phonological, tone, and other linguistic errors into a single lexical error. By co…
CWM: An Open-Weights LLM for Research on Code Generation with World Models
FAIR CodeGen team, Jade Copet, Quentin Carbonneaux +48
We release Code World Model (CWM), a 32-billion-parameter open-weights LLM, to advance research on code generation with world models. To improve code understanding beyond what can…
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…
Discrete Flow Matching
Itai Gat, Tal Remez, Neta Shaul +5
Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional…
The Larger the Better? Improved LLM Code-Generation via Budget Reallocation
Michael Hassid, Tal Remez, Jonas Gehring +2
It is a common belief that large language models (LLMs) are better than smaller-sized ones. However, larger models also require significantly more time and compute during inference…