Thanks for the post, I agree with the main points.
There is another claim on causality one could make, which would be: LLMs cannot reliably act in the world as robust agents since by acting in the world, you change the world, leading to a distributional shift from the correlational data the LLM encountered during training.
I think that argument is correct, but misses an obvious solution: once you let your LLM act in the world, simply let it predict and learn from the tokens that it receives in response. Then suddenly, the LLM does not model correlational, but actual causal relationships.
Thanks for the post, I agree with the main points.
There is another claim on causality one could make, which would be: LLMs cannot reliably act in the world as robust agents since by acting in the world, you change the world, leading to a distributional shift from the correlational data the LLM encountered during training.
I think that argument is correct, but misses an obvious solution: once you let your LLM act in the world, simply let it predict and learn from the tokens that it receives in response. Then suddenly, the LLM does not model correlational, but actual causal relationships.