Tag: Supervised Learning

AI Machine Learning & Data Science Research

NVIDIA’s STEERLM Approach: Empowering User-Steerable Language Models

In a new paper SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF, an NVIDIA research team introduces STEERLM, a novel supervised fine-tuning method that empowers end-users to control model responses during inference, surpassing even state-of-the-art baselines, including RLHF models like ChatGPT-3.5.

AI Machine Learning & Data Science Research

DeepMind Studies Process- vs Outcome-based Model Supervision, Significantly Reducing Reasoning Errors on Math Word Problems

In the new paper Solving Math Word Problems With Process- and Outcome-based Feedback, a DeepMind research team conducts the first comprehensive comparison between process- and outcome-based model supervision. The two approaches achieve comparable final-answer error rate improvements on math word problems, while the process-based method significantly reduces reasoning errors from 14.0 to just 3.4 percent.