Sentence Completion
45 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Sentence Completion
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Libraries
Use these libraries to find Sentence Completion models and implementations
HellaSwag
xP3
Most implemented papers
Factuality Enhanced Language Models for Open-Ended Text Generation
nayeon7lee/factualityprompt
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In this work, we measure and improve the factual accuracy of large-scale LMs for open-ended text generation.
Recurrent Memory Networks for Language Modeling
In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data.
CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense
Websail-NU/AQuA
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To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.
HellaSwag: Can a Machine Really Finish Your Sentence?
In this paper, we show that commonsense inference still proves difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset.
Muppet: Massive Multi-task Representations with Pre-Finetuning
We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
joeljang/elm
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Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks.
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
Furthermore, we show that instruction tuning with CoT Collection allows LMs to possess stronger few-shot learning capabilities on 4 domain-specific tasks, resulting in an improvement of +2. 24% (Flan-T5 3B) and +2. 37% (Flan-T5 11B), even outperforming ChatGPT utilizing demonstrations until the max length by a +13. 98% margin.
Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models
LLMs are increasingly powerful and widely used to assist users in a variety of tasks.
