arXiv:2603.11394v3 Announce Type: replace-cross Abstract: Large language models (LLMs) excel on static benchmarks, but their performance across multi-turn conversations, which better reflect real-world usage, remains understudied. Addressing this gap is critical in high-stakes settings like healthcare, where patients and clinicians are turning to LLM chatbots to address their medical inquiries. Here, we introduce the "stick-or-switch" (SoS) framework, which partitions a question-answer space into multiple sequential presentations to model two safety-centric behaviors: conviction (i.e., stickin
As LLMs become more integrated into real-world applications, particularly in sensitive areas, the limitations of their performance in sustained interactions are becoming increasingly apparent and critical to address.
This research highlights a significant reliability gap in current LLM capabilities, especially in multi-turn conversations, directly impacting their trustworthiness and applicability in high-stakes domains like healthcare.
The understanding of LLM reliability shifts from static benchmark performance to dynamic conversational fluency, requiring new evaluation frameworks and development priorities for robust AI agent behavior.
- · AI safety researchers
- · LLM developers focused on robustness
- · Healthcare technology providers integrating LLMs
- · High-stakes application developers
- · LLM providers with only benchmark-optimized models
- · Applications relying on naive LLM conversational capabilities
- · Uncritically deployed LLM-based solutions
Increased focus on conversational reliability and safety frameworks for LLMs.
Demand for new LLM architectures or fine-tuning methods specifically designed for multi-turn conversational stability and consistency.
Regulatory bodies may begin to impose specific reliability standards for LLMs used in sensitive real-world applications.
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Read at arXiv cs.LG