LLMs 群體思維嚴重，Springboards 推出 Flint 打破僵局
springboards 推出了 llm 模型 flint，專注於隨機性而非減少幻覺，旨在解決主流模型在開放式查詢中輸出趨於一致的'群體思維'問題。
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.'