Training Socially Aligned Language Models on Simulated Social Interactions
This addresses the issue of AI systems failing to generalize and being vulnerable to attacks, offering a scalable solution for better societal value reflection.
The paper tackled the problem of social alignment in language models by introducing a training paradigm that uses simulated social interactions, resulting in superior performance on alignment benchmarks and human evaluations.
Social alignment in AI systems aims to ensure that these models behave according to established societal values. However, unlike humans, who derive consensus on value judgments through social interaction, current language models (LMs) are trained to rigidly replicate their training corpus in isolation, leading to subpar generalization in unfamiliar scenarios and vulnerability to adversarial attacks. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions. In comparison to existing methodologies, our approach is considerably more scalable and efficient, demonstrating superior performance in alignment benchmarks and human evaluations. This paradigm shift in the training of LMs brings us a step closer to developing AI systems that can robustly and accurately reflect societal norms and values.