Semanlink - Language Model AND Allen Institute for AI (AI2)

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Language Model AND Allen Institute for AI (AI2)

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20 Documents

Self-RAG: Learning to Retrieve, Generate and Critique through Self-Reflection

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2023-10-20 About

[2307.13269] LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

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2023-08-08 About

[2303.17651] Self-Refine: Iterative Refinement with Self-Feedback

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2023-04-03 About

[2303.14177] Scaling Expert Language Models with Unsupervised Domain Discovery

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2023-03-27 About

[2212.09741] One Embedder, Any Task: Instruction-Finetuned Text Embeddings

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2023-02-17 About

Embedding Recycling: Making Language Model Development More Sustainable | AI2 Blog

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2023-02-17 About

[2209.01975] Selective Annotation Makes Language Models Better Few-Shot Learners

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2022-09-07 About

[2106.10199] BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

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2022-09-01 About

(((ل()(ل() 'yoav))))👾 sur Twitter : "... another step in understanding how transformer-based LMs work..."

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2022-03-30 About

[2004.10964] Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

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2020-12-01 About

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 About

[1906.07241] Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling

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2020-05-11 About

Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019

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2020-01-05 About

[1902.11269] Efficient Contextual Representation Learning Without Softmax Layer

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**how to accelerate contextual representation learning**.

> Contextual representation models are difficult to train due to the large parameter sizes and high computational complexity

> We find that the softmax layer (the output layer) causes significant inefficiency due to the large vocabulary size.
Therefore, we redesign the learning objectiv.
> Specifically, the proposed approach bypasses the softmax layer by performing language modeling with dimension reduction, and allows the models to leverage pre-trained word embeddings.
Our framework reduces the time spent on the output layer to a negligible level, eliminates almost all the trainable parameters of the softmax layer and performs language modeling without truncating the vocabulary.
When applied to ELMo, our method achieves a 4 times speedup and eliminates 80% trainable parameters while achieving competitive performance on downstream tasks.

**decouples learning contexts and words**

> Instead of using
a softmax layer to predict the distribution of the
missing word, we utilize and extend the SEMFIT
layer (Kumar and Tsvetkov, 2018) to **predict the
embedding of the missing word**.

2019-03-02 About

The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar

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2018-12-03 About

[1811.05370] Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents

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2018-11-20 About

iliaschalkidis/ELMo-keras: Re-implementation of ELMo on Keras

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2018-11-14 About

AI Can Recognize Images, But Text Has Been Tricky—Until Now | WIRED

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2018-09-08 About

[1806.06259] Evaluation of sentence embeddings in downstream and linguistic probing tasks

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2018-06-19 About

ELMo: Deep contextualized word representations (2018)

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ELMo

2018-02-16 About