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Retrieval-augmented generation (RAG) enables large language models to generate better output by retrieving documents that are relevant to a user’s prompt. Fine-tuning further improves RAG performance.

The combination of  language models that are equipped for retrieval augmented generation can retrieve text from a database to improve their output. Further work extends this capability to retrieve information from any application that comes with an API. 

A novel twist on self-supervised learning aims to improve on earlier methods by helping vision models learn how parts of an image relate to the whole.

Meta is rolling out AI-powered upgrades to its social platforms. Meta announced a chat interface, image generator, and celebrity tie-ins for Facebook, Instagram, Messenger, and WhatsApp. The new capabilities take advantage of LLaMa 2 and an unnamed image generator, presumably CM3leon.

The ability of OpenAI’s CLIP to produce similar embeddings of a text phrase and a matching image opened up applications like classifying images according to labels that weren’t in the training set. A new model extends this capability to seven data types.

Text-to-video generation is so 2022! A new system takes in text and generates an animated 3D scene that can be viewed or rendered from any angle.

In the absence of nationwide laws that regulate AI, major U.S. tech companies pledged to abide by voluntary guidelines — most of which they may already be following.

While Microsoft and Google scramble to supercharge their businesses with text generation, Meta has yet to launch a flagship generative AI service. Reporters went looking for reasons why.

Text from current language models can be useful as a rough draft, but that leaves the polishing to human writers. A language model learned how to generate and respond to editorial directions.

Meta’s effort to make a large language model available to researchers ended with its escape into the wild. Soon after Meta started accepting applications for developer access to LLaMA, a family of trained large language models...

Autonomous robots trained to navigate in a simulation often struggle in the real world. New work helps bridge the gap in a counterintuitive way.

Large datasets often contain overly similar examples that consume training cycles without contributing to learning. A new paper identifies similar training examples, even if they’re not labeled.

Who would disagree that, if all people are mortal and Socrates is a person, Socrates must be mortal? GPT-3, for one. Recent work shows that bigger language models are not necessarily better when it comes to logical reasoning.

The important question of how companies and organizations use our data has received a lot of attention in the technology and policy communities. An equally important question that deserves more focus in 2023 is how...

Large language models are trained only to predict the next word based on previous ones. Yet, given a modest fine-tuning set, they acquire enough information to learn how to perform tasks such as answering questions.