activity
20192026
most citedBeyond English-Centric Multilingual Machine Translation

470 citations · 1.7k across the 40 of their papers we have counts for

collaborators
Showing 2024Show all

9 papers · 1 filter

cs.CL2024

POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference Optimization

Batuhan K. Karaman, Ishmam Zabir, Alon Benhaim +3

Achieving both high safety and high usefulness simultaneously in large language models has become a critical challenge in recent years.Models often exhibit unsafe behavior or adopt…

cs.CL2024

Scaling Laws for Multilingual Language Models

Yifei He, Alon Benhaim, Barun Patra +6

We propose a novel scaling law for general-purpose decoder-only language models (LMs) trained on multilingual data, tackling the problem of balancing languages during multilingual…

cs.CL2024

GRIN: GRadient-INformed MoE

Liyuan Liu, Young Jin Kim, Shuohang Wang +14

Mixture-of-Experts (MoE) models scale more effectively than dense models due to sparse computation through expert routing, selectively activating only a small subset of expert modu…

cs.LG2024

Scaling Optimal LR Across Token Horizons

Johan Bjorck, Alon Benhaim, Vishrav Chaudhary +2

State-of-the-art LLMs are powered by scaling -- scaling model size, dataset size and cluster size. It is economically infeasible to extensively tune hyperparameter for the largest…

cs.CL2024

S2-Attention: Hardware-Aware Context Sharding Among Attention Heads

Xihui Lin, Yunan Zhang, Suyu Ge +5

Sparse attention, which selectively attends to a subset of tokens in the context was supposed to be efficient. However, its theoretical reduction in FLOPs has rarely translated int…

cs.CL2024

A Practical Analysis of Human Alignment with *PO

Kian Ahrabian, Xihui Lin, Barun Patra +4

At the forefront of state-of-the-art human alignment methods are preference optimization methods (*PO). Prior research has often concentrated on identifying the best-performing met…

cs.CL2024

sPhinX: Sample Efficient Multilingual Instruction Fine-Tuning Through N-shot Guided Prompting

Sanchit Ahuja, Kumar Tanmay, Hardik Hansrajbhai Chauhan +9

Despite the remarkable success of large language models (LLMs) in English, a significant performance gap remains in non-English languages. To address this, we introduce a novel app…

cs.CL2024★ 163 cited

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Marah Abdin, Jyoti Aneja, Hany Awadalla +126

We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal test…

cs.CV2024

ODIN: A Single Model for 2D and 3D Segmentation

Ayush Jain, Pushkal Katara, Nikolaos Gkanatsios +5

State-of-the-art models on contemporary 3D segmentation benchmarks like ScanNet consume and label dataset-provided 3D point clouds, obtained through post processing of sensed multi…