Reading Between the Lines: The One-Sided Conversation Problem

2025-11-04 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A new study formalizes the "one-sided conversation problem" (1SC), where conversational AI must operate with only one speaker's dialogue recorded, common in telemedicine, call centers, and smart glasses. Researchers investigated two tasks: reconstructing missing speaker turns for real-time applications and generating summaries from one-sided transcripts. Evaluating both prompting and finetuned models on datasets like MultiWOZ, DailyDialog, and Candor, they found that providing one future turn and utterance length data improves reconstruction accuracy. Placeholder prompting effectively reduces hallucination in generated turns. While large language models (LLMs) show promise with prompting for reconstruction, smaller models necessitate finetuning. The study also determined that high-quality summaries can be produced directly from one-sided transcripts without needing to reconstruct the missing turns first, marking a step towards privacy-aware conversational AI.

Key takeaway

For research scientists developing conversational AI in privacy-sensitive domains, you should consider the 1SC framework to address scenarios where only one side of a dialogue is available. Prioritize incorporating future turn information and utterance length into your reconstruction models, and explore placeholder prompting to enhance output reliability. Remember that high-quality summaries are achievable without full dialogue reconstruction, potentially simplifying your pipeline for certain applications.

Key insights

One-sided conversation challenges in AI can be addressed by reconstructing missing turns or directly summarizing.

Principles

• Future turn context improves reconstruction.
• Placeholder prompting mitigates hallucination.
• Summaries don't require full reconstruction.

Method

The method involves evaluating prompting and finetuned models on 1SC tasks, specifically reconstructing missing turns and generating summaries from one-sided transcripts, using human A/B testing and LLM-as-a-judge metrics.

In practice

• Use future turn context for better reconstruction.
• Employ placeholder prompting to reduce AI hallucination.
• Finetune smaller models for 1SC tasks.

Topics

• One-Sided Conversation Problem
• Conversational AI
• Missing Turn Reconstruction
• One-Sided Summarization
• Large Language Models

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.