When Stability Fails: Hidden Failure Modes Of LLMS in Data-Constrained Scientific Decision-Making
#LLMs #failure modes #scientific decision-making #data-constrained #stability #overconfidence #validation
📌 Key Takeaways
- LLMs exhibit hidden failure modes in data-constrained scientific decision-making scenarios.
- Stability of LLM outputs can degrade unpredictably when data is limited or noisy.
- These failures may lead to overconfidence in incorrect scientific conclusions.
- The study highlights risks of relying on LLMs for critical scientific decisions without robust validation.
📖 Full Retelling
arXiv:2603.15840v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly used as decision-support tools in data-constrained scientific workflows, where correctness and validity are critical. However, evaluation practices often emphasize stability or reproducibility across repeated runs. While these properties are desirable, stability alone does not guar- antee agreement with statistical ground truth when such references are available. We introduce a controlled behavioral
🏷️ Themes
AI Reliability, Scientific Research
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Original Source
arXiv:2603.15840v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly used as decision-support tools in data-constrained scientific workflows, where correctness and validity are critical. However, evaluation practices often emphasize stability or reproducibility across repeated runs. While these properties are desirable, stability alone does not guar- antee agreement with statistical ground truth when such references are available. We introduce a controlled behavioral
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