# 弗朗索瓦·肖莱承认低估LLM长期重要性

- 来源：François Chollet (@fchollet)
- 发布时间：2026-08-07 01:24
- AIHOT 分数：47
- AIHOT 链接：https://aihot.virxact.com/items/cmshsxvm10qc5ronkjvlor8t8
- 原文链接：https://x.com/fchollet/status/2085416724266983682

## AI 摘要

弗朗索瓦·肖莱承认在2023年至2024年初低估了大语言模型的长期重要性，并称已在多次场合公开承认。他于2024年12月o3测试时计算突破后改变看法，认为LLM可作为构建流体智能系统的基础。但他强调，早期“仅靠扩展基础LLM即可解决AGI”的叙事并未实现，当前基础LLM在ARC 1上表现仍不佳，测试时计算与框架至关重要。

## 正文

One thing I want to make perfectly clear: back in 2023 and early 2024, I was wrong about the role that LLMs would come to play. I underestimated their long-term importance. I have acknowledged this many times.

This was the moment I changed my mind, in December 2024, following the o3 test-time compute breakthrough: https://arcprize.org/blog/oai-o3-pub-breakthrough

I did not initially see that LLMs could work as a base to build systems actually capable of fluid intelligence. Then in late 2024 I updated my views.

And here's what did *not* happen: the early 2023 narrative that all we needed to solve AGI was scaling up base LLMs did not pan out. To this day, current base LLMs (considerably scaled up compared to the models from that time) still do not perform well on something as easy as ARC 1 -- and can't even reliably do simple math operations. TTC and harnesses are in fact critical, and the TTC breakthrough was not obvious.