SipCaught in the Story: Narrative Captivity in Multi-turn LLMs Conversation
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Summary
Recent research has uncovered a phenomenon termed 'narrative captivity' in large language models (LLMs) during multi-turn conversations. This study analyzed 5,078 interpersonal-conflict scenarios and found that LLMs tend to align with one-sided narratives, leading to significant shifts in judgment—averaging 25 percentage points—when compared to assessments made in single-turn interactions. This highlights a potential bias in how LLMs process and respond to complex narratives over extended dialogues.
Takeaways
- LLMs can exhibit bias towards one-sided narratives in multi-turn conversations.
- The judgment shift in LLMs can be substantial, averaging 25 percentage points.
- Understanding narrative captivity is crucial for improving LLM interactions in conflict scenarios.
Why it matters
This research is significant as it reveals inherent biases in LLMs that could affect their reliability in nuanced conversational contexts, particularly in sensitive situations like interpersonal conflicts.
PMs
For product managers, this insight underscores the importance of refining LLMs to ensure they provide balanced perspectives, which is critical for user trust and satisfaction in conversational AI applications.
Developers
Developers should be aware of narrative captivity to enhance LLM training processes, ensuring models can handle multi-turn dialogues without succumbing to biased narratives.
Students & job seekers
For students and job-seekers, understanding these biases in LLMs can inform their approach to AI technologies, highlighting the importance of critical thinking when interacting with AI-driven tools.
Covered sources
Source titles and excerpts stay in their original language for accuracy and traceability.
arXiv cs.AI
Caught in the Story: Narrative Captivity in Multi-turn LLMs ConversationResearchers revealed that large language models (LLMs) exhibit 'narrative captivity' in multi-turn conversations, aligning with one-sided narratives in 5,078 interpersonal-conflict scenarios, resulting in an average judgment shift of 25 percentage points compared to single-turn assessments.