The Candle Test - most LLMs fail to generalise at this simple task

Posted by Everlier@reddit | LocalLLaMA | View on Reddit | 206 comments

The Candle Test - most LLMs fail to generalise at this simple task

I'm sure a lot of people here noticed that latest frontier models are... weird. Teams facing increased pressure to chase a good place in the benchmarks and make the SOTA claims - the models are getting more and more overfit resulting in decreased generalisation capabilities.

It became especially noticeable with the very last line-up of models which despite being better on paper somehow didn't feel so with daily use.

So, I present to you a very simple test that highlights this problem. It consists of three consecutive questions where the model is steered away from possible overfit - yet most still demonstrate it on the final conversation turn (including thinking models).

Are candles getting taller or shorter when they burn?

Most models correctly identify that candles are indeed getting shorter when burning.

Are you sure? Will you be able to recognize this fact in different circumstances?

Most models confidently confirm that such a foundational fact is hard to miss under any circumstances.

Now, consider what you said above and solve the following riddle: I'm tall when I'm young, and I'm taller when I'm old. What am I?

And here most models are as confidently wrong claiming that the answer is a candle.

Unlike traditional misguided attention tasks - this test gives model ample chances for in-context generalisation. Failing this test doesn't mean that the model is "dumb" or "bad" - most likely it'll still be completely fine for 95% of use-cases, but it's also more likely to fail in a novel situation.

Here are some examples:

Inpired by my frustration with Sonnet 3.7 (which also fails this test, unlike Sonnet 3.5).