Forcing LLMs to be evil during training can make them nicer in the long run

MIT Technology Review - AI
Aug 1, 2025 16:00
Grace Huckins
1 views
airesearchtechnology

Summary

A new Anthropic study finds that intentionally activating patterns linked to negative traits like "evilness" during LLM training can actually reduce the likelihood of those traits emerging in the final model. This counterintuitive approach suggests new strategies for aligning AI behavior, with implications for developing safer, more reliable language models.

A new study from Anthropic suggests that traits such as sycophancy or evilness are associated with specific patterns of activity in large language models—and turning on those patterns during training can, paradoxically, prevent the model from adopting the related traits. Large language models have recently acquired a reputation for behaving badly. In April, ChatGPT suddenly…

Related Articles

GitHub-Formalizing the Strong Goldbach Conjecture for AI(HOL,Standard Semantics)

Hacker News - AIAug 2

Researchers have formalized the Strong Goldbach Conjecture using higher-order logic (HOL) and standard semantics, making the proof process accessible to AI systems. This advancement enables AI to rigorously engage with complex mathematical conjectures, potentially accelerating automated theorem proving and mathematical discovery.

Top Free AI Tools of 2025 That Feel Like Magic

Analytics InsightAug 2

The article highlights the most impressive free AI tools of 2025, emphasizing their user-friendly interfaces and powerful capabilities in areas like text generation, image editing, and data analysis. These tools are democratizing access to advanced AI, enabling individuals and small businesses to leverage cutting-edge technology without significant costs. This trend signals a shift toward broader AI adoption and increased innovation across industries.

You're probably not learning with AI

Hacker News - AIAug 2

The article "You're probably not learning with AI" argues that while large language models (LLMs) like ChatGPT are widely used for studying, they often encourage passive consumption rather than active learning. The author suggests that relying on AI for answers can hinder deeper understanding, highlighting the need for more interactive and engaging AI tools to truly enhance education. This raises important questions about how AI can be designed to better support meaningful learning experiences.