Quality-Controlled Reply and Summary Data for Post-Training Small Language Models in AI Call-Screening Agents

Key points

• Small language models have improved to the point where a phone can plausibly screen its own calls, answering on an absent owner's behalf without sending call content off the device, a privacy-respecting alternative to cloud call handling.1
• Realizing that promise takes a model that can do two things well: reply within the owner's authority during the call, and afterward tell the owner what happened and what needs attention.1
• Released small models do neither reliably - they raise unwarranted alarms about ordinary callers and write summaries that drop material conditions or report a caller's claims as fact - and real calls supply no reference replies or summaries to learn from.1
• Training a single shared LoRA adapter on both tasks, we evaluate six models (0.6-4B parameters) before and after adaptation on 192 held-out calls.1

Sources (1)

• [1]Quality-Controlled Reply and Summary Data for Post-Training Small Language Models in AI Call-Screening Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 06:42 PM

  We construct supervision for both tasks from synthetic calls under an information-boundary constraint: a caller writer works from a private scenario brief, a reply writer sees only the conversation up to the current turn, and a summary writer sees the completed transcript.

  Small language models have improved to the point where a phone can plausibly screen its own calls, answering on an absent owner's behalf without sending call content off the device, a privacy-respecting alternative to cloud call handling.

Extractive summary: sentences quoted from the sources.