Understanding BLOOM: An empirical study on diverse NLP tasks

27 November 2022

Papers citing "Understanding BLOOM: An empirical study on diverse NLP tasks"

8 / 8 papers shown

Title
Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set Florian Eichin Y. Liu Barbara Plank Michael A. Hedderich 39 0 0 13 Mar 2025
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA Zhengyuan Yang Zhe Gan Jianfeng Wang Xiaowei Hu Yumao Lu Zicheng Liu Lijuan Wang 169 402 0 10 Sep 2021
Unifying Vision-and-Language Tasks via Text Generation Jaemin Cho Jie Lei Hao Tan Mohit Bansal MLLM 249 525 0 04 Feb 2021
The Pile: An 800GB Dataset of Diverse Text for Language Modeling Leo Gao Stella Biderman Sid Black Laurence Golding Travis Hoppe ... Horace He Anish Thite Noa Nabeshima Shawn Presser Connor Leahy AIMat 245 1,977 0 31 Dec 2020
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,913 0 31 Dec 2020
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models Phillip Rust Jonas Pfeiffer Ivan Vulić Sebastian Ruder Iryna Gurevych 69 234 0 31 Dec 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,424 0 23 Jan 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 294 6,943 0 20 Apr 2018