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SQuARE: Towards Multi-Domain and Few-Shot Collaborating Question Answering Agents

ODSC - Open Data Science

Are you curious about explainability methods like saliency maps but feel lost about where to begin? Plus, our built-in QA ecosystem , including explainability, adversarial attacks, graph visualizations, and behavioral tests, allows you to analyze the models from multiple perspectives. Don’t worry, you’re not alone! Euro) in 2021.

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A Gentle Introduction to GPTs

Mlearning.ai

GPT-3 is a autoregressive language model created by OpenAI, released in 2020 . OpenAI’s research paper on the GPT-3, “Language Models are Few-Shot Learners” was released in May 2020 and it outlined the fact that state-of-the-art GPT-3 generated text is nearly indistinguishable to that of the text written by humans. What is GPT-3?

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AI2 at ACL 2023

Allen AI

options that were not); 2) evaluate the quality of that caption by scoring it more highly than a lower quality option from the same contest; and 3) explain why the joke is funny. The *CL conferences created the NLP Reproducibility Checklist in 2020 to be completed by authors at submission to remind them of key information to include.

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ML and NLP Research Highlights of 2021

Sebastian Ruder

  While pre-trained transformers will likely continue to be deployed as standard baselines for many tasks, we should expect to see alternative architectures particularly in settings where current models fail short, such as modeling long-range dependencies and high-dimensional inputs or where interpretability and explainability are required.

NLP 52
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68 Summaries of Machine Learning and NLP Research

Marek Rei

link] Proposes an explainability method for language modelling that explains why one word was predicted instead of a specific other word. Adapts three different explainability methods to this contrastive approach and evaluates on a dataset of minimally different sentences. Computational Linguistics 2022.

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Explosion in 2019: Our Year in Review

Explosion

Adriane is a computational linguist who has been engaged in research since 2005, completing her PhD in 2012. In this episode he explained how to transition a rule-based prototype towards an NER model to achieve faster results and a baseline for machine learning experiments. ? Thanks for all your support!

NLP 52