这篇论文用HIVE工具对比语音和键盘输入的扰动，发现语音转写对模型影响更大，键盘错字反而扛得住。想知道输入方式怎么影响LLM表现可以看看。
论文提出HIVE（Human Input-Variation Engine）输入扰动测试套件，覆盖语音转写扰动和QWERTY键盘扰动。HIVE实验显示，语音转写扰动让所有被测指令微调模型的准确率下降，成本主要来自转写结构而非填充词。在HIVE的键盘扰动测试里，模型吸收大量QWERTY噪声后准确率才明显下降，两类扰动的影响都取决于问题中token被破坏的数量。七项发现中的第六项指出，思考预算能基本恢复键盘通道的准确率，但对语音转写无效。压缩语音在思考预算下反而更差，该结果来自HIVE的第七项发现。
Should We Type or Talk to LLM Agents? A Comprehensive Study of Voice and Keyboard Input Perturbations
Human input reaches language models by typing or speaking, and each channel leaves a distinct signature: orthographic noise for keyboards; for voice, disfluency from conventional transcription and restructuring from AI-backed dictation tools. How do they impact an LLM's performance? In this paper we present HIVE (Human Input-Variation Engine), a suite of voice transcription perturbations and QWERTY keyboard perturbations. We use HIVE to evaluate how robust models are to these perturbations. We present seven findings. (i) Voice transcription perturbations lower accuracy across every instruction-tuned model we test, and it is the structure of the transcription rather than its fillers that carries the cost. (ii) QWERTY keyboard perturbations cost less, and a model absorbs a lot of them before accuracy falls away. (iii) Both trace back to one cause, how many of the question's tokens survive the perturbation: destroying a token is what hurts, while adding new ones alongside it costs little. (iv) The gap between the two channels appears only where the answer must be constructed or deduced; on multiple choice there is none. (v) The harm does not solely come from test-set contamination. (vi) It cannot be trained away with lightweight adaptation. (vii) A thinking budget recovers the keyboard channel almost entirely but leaves the spoken registers untouched, and compressed speech is worse with it.