20 stories
Throughout recent years, LLM capabilities have outpaced evaluation benchmarks. This is not a new development. What is new is that the set of standard LLM evals has further narrowed—and there are questions regarding the reliability of even this small set of benchmarks.
This post is an update on what I’ve been up to since I joined Cohere. I’ve had fun contributing to the launch of Command R and R+, the latest Cohere models. I’ll discuss more details once the tech report is out; in the meantime I’ll share what I’m most excited about.
Little over a week ago, Gemini 1.5 reported close to human-level performance on MTOB, a recent challenging translation dataset. In this newsletter, we’ll dig into this result, explore true zero-shot machine translation (MT), and consider how to teach LLMs a new language like humans.
It’s crazy how the AI and NLP landscape has evolved over the last five years. 5 years ago, around the time I finished my PhD, if you wanted to work on cutting-edge natural language processing (NLP), your choice was relatively limited.
More papers on AI are published than ever before1 but each paper tends to only present its part of the picture—and it becomes difficult to recognize the larger story to which a paper is connected.
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.
In this newsletter, I’ll discuss a selection of exciting papers and workshops I’m looking forward to at EMNLP 2023 and the trends I observed.
NeurIPS 2023, arguably this year’s biggest AI conference takes place in two weeks from Dec 10–16 in New Orleans. 3586 papers were accepted to the conference, which are available online.
We cover the latest generation of instruction-tuning datasets that are much closer to real-world use cases—but still have their limitations!
In this post, we’ll take a closer look at the question “Do LMs represent space and time?” inspired by a recent paper.
NLP and ML have gone through several phases of how models are trained in recent years. With the arrival of pre-trained models such as BERT, fine-tuning pre-trained models for downstream tasks became the norm.
In this edition, we’ll explore tool use—arguably one of the hottest new capabilities of LLMs. We’ll look at types of tools, benefits of tool use, recent developments, and future directions.
This newsletter discusses components for building generative agents and publication venues for large language models (LLMs).
This newsletter discusses attention that enables modeling long sequences and simple but surprisingly competitive classifiers such as based on gzip compression.
Welp. Twitter plans to shut down Revue, my previous newsletter platform. I hope you like our new home, Substack. 🤗 I’m actually quite excited about the change. Substack allows comments on posts. We can now have actual discussions rather than just one-on-one email exchanges. Yay!
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,I hope you've had a good start to the new year. This newsletter covers my and others' highlights of 2021. I also discuss recent pre-trained models that put more emphasis on safety and recent text similarity models where large is not always better.
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.
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