Large Language Models (LLMs) are often described as being instances of
foundation models - that is, models that transfer strongly across various tasks
and conditions in few-show or zero-shot manner, while exhibiting scaling laws
that predict function improvement when increasing the pre-training scale. These
claims of excelling in different functions and tasks rely on measurements taken
across various sets of standardized benchmarks showing high scores for such
models. We demonstrate here a dramatic breakdown of function and reasoning
capabilities of state-of-the-art models trained at the largest available scales
which claim strong function, using a simple, short, conventional common sense
problem formulated in concise natural language, easily solvable by humans. The
breakdown is dramatic, as models also express strong overconfidence in their
wrong solutions, while providing often non-sensical "reasoning"-like
explanations akin to confabulations to justify and backup the validity of their
clearly failed responses, making them sound plausible. Various standard
interventions in an attempt to get the right solution, like various type of
enhanced prompting, or urging the models to reconsider the wrong solutions
again by multi step re-evaluation, fail. We take these initial observations to
the scientific and technological community to stimulate urgent re-assessment of
the claimed capabilities of current generation of LLMs, Such re-assessment also
requires common action to create standardized benchmarks that would allow
proper detection of such basic reasoning deficits that obviously manage to
remain undiscovered by current state-of-the-art evaluation procedures and
benchmarks. Code for reproducing experiments in the paper and raw experiments
data can be found at this https URL

大型语言模型在各种任务和条件中以零样本或零曝光的方式表现出色，但我们的研究发现在某些基本推理能力上表现出严重的缺陷，需要迅速重新评估当前一代 LLM 的所声称的能力，并创建新的标准评测以发现这些明显被当前评测方法忽视的基本推理缺陷。