Do Large Language Models Always Tell The Same Stories?
📰 ArXiv cs.AI
Discover how large language models generate stories and learn to evaluate their narrative similarity using a contrastive framework
Action Steps
- Collect a dataset of human-written stories and prompts from sources like r/WritingPrompts
- Implement a contrastive framework to evaluate narrative similarity between LLM-generated stories
- Train and fine-tune a large language model using the collected dataset
- Generate stories using the trained model and evaluate their diversity
- Compare the narrative similarity of LLM-generated stories with human-written stories
Who Needs to Know This
NLP researchers and engineers can benefit from understanding the capabilities and limitations of large language models in generating diverse stories, while product managers can apply this knowledge to develop more engaging language-based products
Key Insight
💡 Large language models can generate high-quality prose, but their ability to produce diverse outputs is still uncertain and requires further evaluation using frameworks like narrative similarity
Share This
🤖 Can large language models tell different stories? 📚 Researchers investigate narrative similarity in LLM-generated prose 📊
Key Takeaways
Discover how large language models generate stories and learn to evaluate their narrative similarity using a contrastive framework
Full Article
Title: Do Large Language Models Always Tell The Same Stories?
Abstract:
arXiv:2606.17350v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet the question of whether these models are capable of generating diverse outputs remains contested. In this work, we investigate the diversity of LLM-generated stories through the framework of narrative similarity. Using a contrastive framework and a dataset of human-written stories and prompts from r/WritingPrompts, we collect narrative similarit
Abstract:
arXiv:2606.17350v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet the question of whether these models are capable of generating diverse outputs remains contested. In this work, we investigate the diversity of LLM-generated stories through the framework of narrative similarity. Using a contrastive framework and a dataset of human-written stories and prompts from r/WritingPrompts, we collect narrative similarit
DeepCamp AI