Summary of Collabstory: Multi-llm Collaborative Story Generation and Authorship Analysis, by Saranya Venkatraman et al.
CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis
by Saranya Venkatraman, Nafis Irtiza Tripto, Dongwon Lee
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper explores the possibility of Large Language Models (LLMs) collaborating on open-ended writing tasks. The authors introduce CollabStory, a dataset generated by multiple LLMs co-authoring stories, with scenarios ranging from single-author to multi-author (up to 5 LLMs). The dataset comprises over 32k stories produced using open-source instruction-tuned LLMs. Building upon human-human multi-author writing tasks and analysis, the paper extends these authorship-related tasks for multi-LLM settings and presents baselines for LLM-LLM collaboration. However, the current baselines are found to be inadequate for this emerging scenario. The authors emphasize the importance of understanding and developing techniques to discern multiple LLMs’ contributions, as LLM-LLM collaboration may overwhelm challenges related to plagiarism detection, credit assignment, academic integrity, and copyright infringement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a future where computers can work together to create new stories. This paper takes the first step in exploring this idea by having multiple computer language models collaborate on writing tasks. The authors created a huge dataset of over 32,000 stories written by these computer models working together. They also developed ways to analyze how well these models work together and what makes their collaborations successful or unsuccessful. This research is important because it could help us understand how computers can work together more effectively in the future, which could have big implications for things like plagiarism detection, credit assignment, and maintaining academic integrity. |
