Reading Between the Lines: The One-Sided Conversation Problem

Researchers formalize the one-sided conversation problem (1SC), where only one participant's dialogue can be recorded—common in telemedicine, call centers, and smart glasses. The study evaluates methods to reconstruct missing speaker turns and generate summaries from incomplete transcripts, finding that smaller models require finetuning while larger models show promise with prompting techniques.

This research addresses a genuine technical challenge in conversational AI deployment where privacy constraints or hardware limitations prevent recording both dialogue participants. The one-sided conversation problem reflects real-world constraints rather than theoretical edge cases, making this work practically relevant for industries handling sensitive audio data. Healthcare providers, customer service operations, and wearable device manufacturers frequently encounter scenarios where capturing full conversations raises privacy or compliance concerns.

The study's findings reveal nuanced tradeoffs in reconstruction and summarization approaches. Access to future context and utterance length information significantly improves reconstruction quality, while placeholder prompting reduces hallucination—a critical issue when models generate plausible but incorrect dialogue. The performance gap between large and small models suggests that parameter scale enables better inference from incomplete information, though finetuning provides a viable path for resource-constrained deployments.

The ability to generate high-quality summaries without reconstructing missing turns has direct commercial implications. Organizations can extract meaningful insights from one-sided conversations without the computational overhead or accuracy risks of full dialogue reconstruction, streamlining compliance reporting and quality assurance workflows. This approach balances utility with privacy—a increasingly valuable positioning as regulatory frameworks tighten around conversational data.

The research establishes 1SC as a distinct research problem rather than a marginal limitation, potentially spurring development of specialized architectures and training methods. Future work likely focuses on domain-specific fine-tuning, improved hallucination detection, and real-time performance optimization for live applications like telemedicine consultations.

• →One-sided conversation problem emerges as distinct technical challenge affecting telemedicine, call centers, and smart glasses applications
• →Smaller models require finetuning for dialogue reconstruction while larger models achieve reasonable results through prompting alone
• →Placeholder prompting and access to future context significantly reduce hallucination and improve reconstruction accuracy
• →High-quality summaries can be generated from incomplete transcripts without attempting full dialogue reconstruction
• →Privacy-aware conversational AI gains practical utility through successful handling of single-speaker transcript scenarios