Earlier quoted context omitted.
>With the discovery that transformers lack reasoning capabilities The only paper I have seen claiming this studied only lightweight open-source models (<27B, mostly 2B and 8B). The also included o1 and 4o for reference, which kind of broke their hypothesis, but they just left that part out of the conclusion. Not even kidding, their graphs show o1 and 4o having strong performance in their benchmarks, but the conclusio…
https://arxiv.org/abs/2410.05229 An 18% drop in accuracy (figure 8) is not insignificant. Even 4o suffered 10% loss (figure 6), and 4o isn't a small llm. Competent performance should have near zero performance loss. The simplest benchmark merely changes things like "john had 4 apples" to "Mary had 4 oranges." Performance loss due to inconsequential tokens changing is the very definition of over-fitting.
Those models (4o, o1-mini, preview) don't see any drop at all on those benchmarks. The only benchmark that see drops with the SOTA models is the one they add, "seemingly relevant but ultimately irrelevant information".
Humans can and do drop in performance when presented with such alterations. Are they better than LLMs in that case ? Who knows ? Because these papers don't bother testing human baselines.