Summary of Towards Realistic Synthetic User-generated Content: a Scaffolding Approach to Generating Online Discussions, by Krisztian Balog and John Palowitch and Barbara Ikica and Filip Radlinski and Hamidreza Alvari and Mehdi Manshadi
Towards Realistic Synthetic User-Generated Content: A Scaffolding Approach to Generating Online Discussions
by Krisztian Balog, John Palowitch, Barbara Ikica, Filip Radlinski, Hamidreza Alvari, Mehdi Manshadi
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the creation of realistic, large-scale synthetic datasets of user-generated content, specifically social media discussion threads. The authors demonstrate that straightforward application of large language models (LLMs) is limited in capturing online discussions’ complex structure and propose a multi-step generation process, using compact representations referred to as scaffolds. The framework is adaptable to specific social media platforms and is demonstrated using data from two distinct online discussion platforms. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates synthetic social media discussions that are realistic and large-scale. It uses big language models (LLMs) to generate responses like people do online, but these LLMs don’t always get it right. So, the authors come up with a new way to make fake conversations look more real by breaking them down into smaller pieces called scaffolds. This helps create more realistic and diverse discussions that are similar to what you see on social media. |