NLP | ACL·EMNLP·NAACL·EACL·COLING 학회 완전 비교
자연어처리분야 글로벌 탑티어 학회인 ACL·EMNLP·NAACL·EACL·COLING 에 대해 대해 종합적으로 비교 분석합니다. 이 글은 NLP 연구를 준비하는 대학원생들이 본인의 연구 주제와 목표에 따라 어떤 학회를 겨냥할지 전략적으로 판단하는 데 도움을 주는 것을 목적으로 합니다.
32 stories
자연어처리분야 글로벌 탑티어 학회인 ACL·EMNLP·NAACL·EACL·COLING 에 대해 대해 종합적으로 비교 분석합니다. 이 글은 NLP 연구를 준비하는 대학원생들이 본인의 연구 주제와 목표에 따라 어떤 학회를 겨냥할지 전략적으로 판단하는 데 도움을 주는 것을 목적으로 합니다.
In this tutorial, we look at the TextAttack framework for NLP data augmentation, adversarial training, and adversarial attacks.
Learn about a variety of techniques used to keep deep learning NLP models secure.
Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes.
'Vec2text' can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.
NLP research has undergone a paradigm shift over the last year. A range of large language models (LLMs) has validated the unreasonable effectiveness of scale1.
The use of large language models in the real world has strongly accelerated by the launch of ChatGPT. We (including my team at OpenAI, shoutout to them) have invested a lot of effort to build default safe behavior into the model during the alignment process (e.g. via RLHF).
Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples.
Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights.
Hi all,This newsletter covers PaLM, DALL-E 2, and Chinchilla, chain-of-thought prompting, and the role of values and culture in NLP.This edition is somewhat delayed as I've been busy with planning a move (I'll be flying 🛫 to Germany tomorrow; say hi 👋 if you're in Berlin) and exhausted by current…
Hi all, First off, some personal news: I've moved from DeepMind to Google Research this week. Because of this move, the past months have been quite busy. In light of this, I decided to pause the newsletter over the last couple of months.
Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior and biases from the Internet. Pretrained language models are very powerful and have shown great success in many NLP tasks.
[Updated on 2021-02-01: Updated to version 2.0 with several work added and many typos fixed.] [Updated on 2021-05-26: Add P-tuning and Prompt Tuning in the “prompt design” section.] [Updated on 2021-09-19: Add “unlikelihood training”.]
[Updated on 2020-11-12: add an example on closed-book factual QA using OpenAI API (beta). A model that can answer any question with regard to factual knowledge can lead to many useful and practical applications, such as working as a chatbot or an AI assistant🤖.
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