Machine Learning Research
More Factual LLMs: FactTune, a method to fine-tune LLMs for factual accuracy without human feedback
Large language models sometimes generate false statements. New work makes them more likely to produce factual output.
Stanford University
Science
Cross-Species Cell Embeddings: AI enhances cell type discovery, identifies previously elusive “Norn cells”
Researchers used an AI system to identify animal cell types from gene sequences, including a cell type that conventional approaches had discovered only in the past year.
Machine Learning Research
Cutting the Cost of Pretrained Models: FrugalGPT, a method to cut AI costs and maintain quality
Research aims to help users select large language models that minimize expenses while maintaining quality.
Machine Learning Research
Learning Language by Exploration: Agent develops language skills through simulated exploration tasks
Machine learning models typically learn language by training on tasks like predicting the next word in a given text. Researchers trained a language model in a less focused, more human-like way.
Machine Learning Research
Robot, Find My Keys: A machine learning model for robots to predict the location of objects in households
Researchers proposed a way for robots to find objects in households where things get moved around. Andrey Kurenkov and colleagues at Stanford University introduced Node Edge Predictor, a model that learned to predict where objects were located in houses.
Machine Learning Research
What We Know — and Don’t Know — About Foundation Models: A new Stanford index to assess the transparency of leading AI models
A new index ranks popular AI models in terms of information their developers provide about their training, architecture, and usage. Few score well.
Machine Learning Research
LLMs Get a Life: The generative agents that mimic human behavior in a simulated town
Large language models increasingly reply to prompts with a believably human response. Can they also mimic human behavior?
Tech & Society
ChatGPT Ain’t What It Used to Be: ChatGPT's behavior change over time
It wasn’t your imagination: OpenAI’s large language models have changed. Researchers at Stanford and UC Berkeley found that the performance of GPT-4 and GPT-3.5 has drifted in recent months. In a limited selection of tasks, some prompts yielded better results than before, some worse.
Machine Learning Research
Bug Finder: A system that provides feedback with near human-level accuracy
One challenge to making online education available worldwide is evaluating an immense volume of student work. Especially difficult is evaluating interactive computer programming assignments such as coding a game.
Machine Learning Research
Three Methods for Detecting Generated Text: Techniques to tell when you're reading AI-generated text
How can you tell when you’re reading machine-generated text? Three recent papers proposed solutions: Watermarking, classification, and a statistical method.
Tech & Society
AI Trends Tracked: 2023's AI trends from Stanford's AI Index
Stanford’s sixth annual AI Index takes stock of a rapidly growing field. The sprawling, 386-page report from the Institute for Human-Centered AI presents the past year’s developments in AI based on a wide variety of sources including benchmarks, papers, market research, job listings, and polls.
Machine Learning Research
Unsupervised Data Pruning: New method removes useless machine learning data.
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.