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ML and NLP Research Highlights of 2021

Sebastian Ruder

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP).   2021 saw the continuation of the development of ever larger pre-trained models. 2021 saw the development of alternative model architectures that are viable alternatives to the transformer. style loss.

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Supporting Human-AI Collaboration in Auditing LLMs with LLMs

ML @ CMU

An algorithm audit 1 is a method of repeatedly querying an algorithm and observing its output in order to draw conclusions about the algorithm’s opaque inner workings and possible external impact. Auditing Algorithms: Understanding Algorithmic Systems from the Outside In Found. Trends Human Computer Interaction. [2]

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AI2 at ACL 2023

Allen AI

This research posits that simply scaling up models will not imbue them with theory of mind due to the inherently symbolic and implicit nature of the phenomenon, and instead investigate an alternative: can we design a decoding-time algorithm that enhances theory of mind of off-the-shelf neural language models without explicit supervision?

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The State of Multilingual AI

Sebastian Ruder

For a recent study [3] , we similarly reviewed papers from ACL 2021 and found that almost 70% of papers only evaluate on English. Initiatives   The Association for Computational Linguistics (ACL) has emphasized the importance of language diversity, with a special theme track at the main ACL 2022 conference on this topic.

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Reward Isn't Free: Supervising Robot Learning with Language and Video from the Web

The Stanford AI Lab Blog

Indeed, this recipe of massive, diverse datasets combined with scalable offline learning algorithms (e.g. Replicating these impressive generalization and adaptation capabilities in robot learning algorithms would certainly be a step toward robots that can be used in unstructured, real world environments. Chen, Suraj Nair, Chelsea Finn.

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Overcoming The Limitations Of Large Language Models

Topbots

400k AI-related online texts since 2021) Disclaimer: This article was written without the support of ChatGPT. We are quick to attribute intelligence to models and algorithms, but how much of this is emulation, and how much is really reminiscent of the rich language capability of humans? Association for Computational Linguistics. [2]

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2022: We reviewed this year’s AI breakthroughs

Applied Data Science

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. This trend started in 2021, with OpenAI Codex , a GPT-3 based tool. How is this even possible?