Summary of Crafting Narrative Closures: Zero-shot Learning with Ssm Mamba For Short Story Ending Generation, by Divyam Sharma et al.
Crafting Narrative Closures: Zero-Shot Learning with SSM Mamba for Short Story Ending Generation
by Divyam Sharma, Divya Santhanam
First submitted to arxiv on: 4 Oct 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 A novel AI-driven storytelling tool that completes stories based on given prompts is presented in this paper. The tool utilizes pre-trained language models to generate conclusions to short story prompts, aiming to alleviate writer’s block and facilitate creative writing. Building upon the success of pre-trained GPT-3.5 and finetuned SSM-Mamba models, the authors explore the intersection of artificial intelligence and creative writing. This research demonstrates the potential for AI-driven creativity in storytelling, with applications beyond writer’s block relief. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new tool is being developed to help people tell stories more easily. Sometimes, when we’re trying to write a story, we might get stuck on what to say next. This tool uses special computer programs to suggest the next part of the story based on what you’ve written so far. It’s not just for writers; anyone can use it to add more ideas to their stories. The researchers used two big language models to make this happen and showed how well they work on a range of tests. |
Keywords
» Artificial intelligence » Gpt
