标题： LLMs能进行数字减法吗？ 显示英文标题

标题： Can LLMs subtract numbers?

摘要： 我们对大型语言模型（LLMs）中的减法进行了系统研究。 尽管之前的基准测试强调了加法和乘法，但减法作为一种非交换运算，在结构上具有独特性，却受到了相对较少的关注。 我们在加法和减法问题上评估了八个预训练的LLMs，这些模型涵盖了四个家族。 我们的实验结果显示，减法的准确性与加法相比存在很大差距。 我们发现，对于（$a-b$）来说，错误集中在（$a<b$）的情况下。 在这些情况下，LLMs通常能正确生成数值大小，但会省略负号。 探查分析表明，LLMs内部会编码结果是否应为负数，但这种信息往往没有反映在生成的输出中。 我们进一步测试了诸如少样本学习和指令调优等著名技术，以查看它们是否能提高LLMs的表现。 我们的结果表明，虽然少样本提示产生了一定的改进，但经过指令调优的模型在生成负号方面达到了接近完美的准确率。 综上所述，这些发现更清晰地描述了LLMs在减法中的算术能力的局限性和可恢复性。 显示英文摘要

摘要： We present a systematic study of subtraction in large language models (LLMs). While prior benchmarks emphasize addition and multiplication, subtraction has received comparatively little attention despite being structurally distinct as a non-commutative operation. We evaluate eight pretrained LLMs spanning four families on addition and subtraction problems. Our experiments reveal that subtraction accuracy lags behind addition by a wide margin. We find that the errors for ($a-b$) are concentrated in cases where ($a<b$). In such cases, LLMs frequently produce the correct magnitude but omit the negative sign. Probing analyses show that LLMs internally encode whether results should be negative, yet this information is often not reflected in generated outputs. We further test well-known techniques such as few-shot learning and instruction-tuning to see if they can improve the LLMs' performance. Our results suggest that while few-shot prompting yields modest gains, the instruction-tuned models achieve near-perfect accuracies in generating the negative sign. Together, these findings provide a clearer characterization of the limitations and recoverability of LLMs' arithmetic capabilities in subtraction.