Summary of Scalable Frame-based Construction Of Sociocultural Normbases For Socially-aware Dialogues, by Shilin Qu et al.
Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues
by Shilin Qu, Weiqing Wang, Xin Zhou, Haolan Zhan, Zhuang Li, Lizhen Qu, Linhao Luo, Yuan-Fang Li, Gholamreza Haffari
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach constructs a Sociocultural Norm (SCN) Base using Large Language Models (LLMs) for socially aware dialogues. The SCN Base is designed to benefit tasks including conversational information retrieval, contextual information retrieval, and retrieval-enhanced machine learning. By leveraging socially aware dialogues enriched with contextual frames as the primary data source, the approach reduces hallucinations and extracts high-quality and nuanced natural-language norm statements. The study also explores the use of synthetic data for annotating dialogue frames, showing that the quality of SCNs derived from synthetic data is comparable to those from real dialogues annotated with gold frames. Furthermore, the extracted SCNs are effective in a RAG-based model for reasoning about multiple downstream dialogue tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sociocultural norms help us behave properly in social situations, and they can even help machines learn better. Researchers created a special database called the Sociocultural Norm Base that uses large language models to understand social cues. This base helps machines have more natural conversations and retrieve information more accurately. The study found that using fake data instead of real conversations works just as well, and the extracted norms can be used in machine learning models for various tasks. |
Keywords
» Artificial intelligence » Machine learning » Rag » Synthetic data
