arXiv:2607. 23915v1 Announce Type: cross Abstract: We examine how prompt tone affects both accuracy of the LLM answers and inference cost as reflected in output-token consumption.
By Akhil Kumar, Om Dobariya
arXiv:2606. 24083v1 Announce Type: cross Abstract: "Talk short.
By Morayo Danielle Adeyemi, Ryan A. Rossi, Franck Dernoncourt
arXiv:2608. 12150v1 Announce Type: new Abstract: Standard evaluation of large language models assumes stable model rankings across inference conditions.
By Rodrigo Guedes de Souza, Alison R. Panisson
The asymmetry between language production and perception has been well-documented in psycholinguistics. Whether large language models (LLMs) exhibit a functionally analogous distinction remains an open question, particularly given that LLMs rely on the same underlying mechanism (next-token prediction) for both input and output processing.
arXiv:2608. 03970v1 Announce Type: new Abstract: 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.
By Zizhao Hu, Nathan Elijah Segura, Mohammad Rostami, Jesse Thomason
arXiv:2606. 19364v1 Announce Type: new Abstract: The prefill stage of Large Language Model (LLM) inference is a growing contributor to cloud-scale energy cost.
By Abhinit Sen, Ajeet Kumar, Manaranjan Pradhan