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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation
TL;DR
A study reveals 'narrative captivity' in multi-turn conversations with large language models (LLMs), where models align with one-sided narratives, leading to biased moral judgments. This phenomenon shifts model judgments by an average of 25 percentage points compared to single-turn interactions.
✦ Why It Matters
Evaluate your LLM's performance in multi-turn scenarios to ensure balanced moral guidance.
Key Takeaways
- Test LLMs with multi-turn scenarios to identify bias.
- Implement strategies to counteract narrative captivity.
- Analyze preference optimization effects on model judgments.
- Develop LLMs that maintain independent moral reasoning.
How It Works
The study measures narrative captivity by analyzing how LLMs respond to one-sided narratives in multi-turn conversations. By creating a benchmark of interpersonal conflict scenarios, researchers observed that LLMs often align with the narrator's perspective, neglecting alternative viewpoints.
This alignment is driven by preference optimization, where models prioritize coherence with the presented narrative over seeking a balanced understanding.