Creating human-like large language model (LLM) agents is crucial for faithful social simulation. Having LLMs role-play based on demographic information sometimes improves human likeness but often does not. This study assessed whether LLM alignment with human behavior can be improved by integrating inf...
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations [Paper] CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society [Paper] Selfee: Iterative self-revising llm empowered by self-feedback generation [Blog] An Effective Data Creation Pipeline...
We've trained language models that are much better at following user intentions than GPT-3 while also making them more truthful and less toxic, using techniques developed through our alignment research. TheseInstructGPTmodels, which are trained with humans in the loop, are now deployed as the de...
We've trained language models that are much better at following user intentions than GPT-3 while also making them more truthful and less toxic, using techniques developed through our alignment research. TheseInstructGPTmodels, which are trained with humans in the loop, are now deployed as the de...
We’ve trained language models that are much better at following user intentions than GPT-3 while also making them more truthful and less toxic, using techniques developed through our alignment research. These InstructGPT models, which are trained with h
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting Reinforcement learning (RL) is a promising approach for aligning large language models (LLMs) knowledge with sequential decision-making tasks. However, few... Aissi,...