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Found 10 papers, 4 papers with code
Exploring the Boundaries of Low-Resource BERT Distillation
We also show that the distillation of large pre-trained models is more effective in real-life scenarios where limited amounts of labeled training are available.
CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity
This approach focuses the reasoning process on generating an attribution-centric output.
Optimizing Retrieval-augmented Reader Models via Token Elimination
Fusion-in-Decoder (FiD) is an effective retrieval-augmented language model applied across a variety of open-domain tasks, such as question answering, fact checking, etc.
Transformer Language Models without Positional Encodings Still Learn Positional Information
Causal transformer language models (LMs), such as GPT-3, typically require some form of positional encoding, such as positional embeddings.
How to Train BERT with an Academic Budget
While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford.
Ranked #19 on
Question Answering
on Quora Question Pairs
Q8BERT: Quantized 8Bit BERT
Recently, pre-trained Transformer based language models such as BERT and GPT, have shown great improvement in many Natural Language Processing (NLP) tasks.
Ranked #13 on
Semantic Textual Similarity
on STS Benchmark
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models
In this work-in-progress we combined the effectiveness of transfer learning provided by pre-trained masked language models with a semi-supervised approach to train a fast and compact model using labeled and unlabeled examples.
Term Set Expansion based NLP Architect by Intel AI Lab
We present SetExpander, a corpus-based system for expanding a seed set of terms into amore complete set of terms that belong to the same semantic class.
SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class.
Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class.
