Reasoning skills of large language models are often overestimated
New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.
New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
This new tool offers an easier way for people to analyze complex tabular data.
This technique could lead to safer autonomous vehicles, more efficient AR/VR headsets, or faster warehouse robots.
LLMs trained primarily on text can generate complex visual concepts through code with self-correction. Researchers used these illustrations to train an image-free computer vision system to recognize real photos.
The SPARROW algorithm automatically identifies the best molecules to test as potential new medicines, given the vast number of factors affecting each choice.
Combining natural language and programming, the method enables LLMs to solve numerical, analytical, and language-based tasks transparently.
The method uses language-based inputs instead of costly visual data to direct a robot through a multistep navigation task.
A new downscaling method leverages machine learning to speed up climate model simulations at finer resolutions, making them usable on local levels.
DenseAV, developed at MIT, learns to parse and understand the meaning of language just by watching videos of people talking, with potential applications in multimedia search, language learning, and robotics.