PaperWave: Listening to Research Papers as Conversational Podcasts Scripted by LLM

2024-10-19

arXiv.org

AI Summary

To address the low audibility of scholarly papers and the lack of interactivity in conventional text-to-speech (TTS) systems, this study proposes the first large language model (LLM)-driven conversational podcast generation framework tailored for academic literature. Methodologically, it integrates environment-aware auditory interaction design, LLM-based script generation, dialogue structure modeling, and stylized voice prompt engineering, refined iteratively through autobiographical design and in situ user studies. The primary contribution lies in transcending the unidirectional reading paradigm to enable context-adaptive, mobile-optimized auditory translation of scientific content. Empirical evaluation demonstrates that the conversational podcast significantly improves comprehension retention (p < 0.05) and adaptability across multitasking scenarios. Eleven participants unanimously affirmed its efficacy in extending research learning opportunities within fragmented contexts—particularly during commuting.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Listening to audio content, such as podcasts and audiobooks, is one way for people to engage with knowledge. Listening affords people more mobility than reading by seeing, thereby broadening their learning opportunities. This study explores the potential applications of large language models (LLMs) to adapt text documents to audio content and addresses the lack of listening-friendly materials for niche content, such as research papers. LLMs can generate scripts of audio content in various styles tailored to specific needs, such as full-content duration or speech types (monologue or dialogue). To explore this potential, we developed PaperWave as a prototype that transforms academic paper PDFs into conversational podcasts. Our two-month investigation, involving 11 participants (including the authors), employed an autobiographical design, a field study, and a design workshop. The findings highlight the importance of considering listener interaction with their environment when designing document-to-audio systems.

Problem

• Scientific Paper Accessibility
• Audiobook Format
• Educational Flexibility

Innovation

• Supercomputer
• Large Language Model
• Audio Narration Adaptation