LLMs 群体思维严重，Springboards 推出 Flint 打破僵局
springboards 推出了 llm 模型 flint，专注于随机性而非减少幻觉，旨在解决主流模型在开放式查询中输出趋于一致的'群体思维'问题。
australian startup springboards has released flint, an llm specifically trained to maximize response variety and mitigate the 'artificial hivemind' effect observed in mainstream models. testing revealed that while dominant models like chatgpt and claude overwhelmingly converge on predictable outputs—such as selecting '7' for random numbers or 'toyota' for car examples—flint successfully generates diverse alternatives like '3.7916' or 'ford f-150.' this development challenges the prevailing industry safety standard of suppressing hallucinations in favor of encouraging controlled randomness for better ideation and creativity. it highlights a growing concern over model homogeneity, a topic recently highlighted in a award-winning paper at neurips regarding the 'open-ended homogeneity of language models.'