LLMはハイブマインドのように振る舞う——そこでSpringboardsがその呪縛を破るFlintを開発
Springboardsは、幻覚（ハルシネーション）の抑制よりもランダム性を重視して学習させたLLM「Flint」を発表。オープンエンドな質問に対して主流モデルが同一の出力に収束してしまう「集団思考（グループシンク）」問題に対処する。
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.'