Sentence Completion
22 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
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
QLoRA: Efficient Finetuning of Quantized LLMs
artidoro/qlora
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Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99. 3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.
Finetuned Language Models Are Zero-Shot Learners
google-research/flan
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ICLR 2022
We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks.
Llama 2: Open Foundation and Fine-Tuned Chat Models
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.
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.
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.
Dependency Recurrent Neural Language Models for Sentence Completion
Recent work on language modelling has shifted focus from count-based models to neural models.
Top-down Tree Long Short-Term Memory Networks
XingxingZhang/td-treelstm
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NAACL 2016
Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have been successfully applied to a variety of sequence modeling tasks.
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
jastfkjg/semantic-matching
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Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.
