The Best of NLP: February 2023’s Top NLP Papers
– For all you NLP enthusiasts out there, here is a list of awesome papers from February 2023 highlighted by C4AI’s research community.
As NLP enthusiasts, we know that this technology is constantly pushing the boundaries of what’s possible. That’s why it’s crucial to stay up-to-date with the latest breakthroughs and advancements. In this post, we’ve curated a selection of the top NLP papers for February 2023, covering a wide range of topics, including the most recent developments in language models, text generation, and summarization.
Our team at Cohere has done the heavy lifting by scouring the web and consulting with our research community to bring you the most current and relevant information on NLP research. We’re thrilled about the progress that NLP has made in recent years, and we can’t wait to see what the future holds. The advancements in this field are enabling us to do more with language than ever before, and this list of top NLP papers will keep you informed and prepared to take advantage of these developments.
At Cohere, our goal is to make NLP technology more accessible to developers and organizations. We believe that the democratization of NLP is key to unlocking its full potential. That’s why we are always looking for new community members to join us on this exciting journey. If you’re passionate about NLP and want to be part of a community that is driving the future of this technology, we would love to have you. Don’t hesitate to apply and be a part of this exciting journey.
These papers were highlighted by C4AI research discord community members. Big thank you to Ujan#3046, bhavnicksm#8949, EIFY#4102, cvf#1006, MajorMelancholy#1836, cakiki#9145, hails#6601, Mike-RsrchRabbit#9843, and the rest of the Cohere For AI NLP research community for participating.
Let’s talk about language models (LMs), which are pretty cool because they can solve new tasks with just a few examples or textual instructions. However, as amazing as they are, LMs sometimes struggle with basic functionality, like doing simple math or finding facts, where smaller models excel. But what if NLP folks could have the best of both worlds? Enter Toolformer!
Toolformer is a model that can teach itself to use external tools via simple APIs. It’s trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. And get this – it does this in a self-supervised way, requiring nothing more than a handful of demonstrations for each API.
Toolformer incorporates a range of tools, including a calculator, a Q&A system, two different search engines, a translation system, and a calendar. And the best part is that it achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities. So, with Toolformer, we’re able to use the best of both worlds, making life a whole lot easier for us NLP, machine learning, AI, and software engineering enthusiasts.
In this paper, the authors tackle the challenge of training large deep learning models with billions of parameters, which is known to require specialized HPC clusters that come with a hefty price tag. To work around this limitation, they explore alternative setups for training these large models, such as using cheap “preemptible” instances or pooling resources from multiple regions.
Then it analyzes the performance of existing model-parallel algorithms in these conditions and identifies configurations where training larger models become less communication-intensive. They introduce SWARM parallelism, a novel model-parallel training algorithm specifically designed for poorly connected, heterogeneous, and unreliable devices.
