A normative-descriptive framework shows LLMs' tool-calling perceptions misalign with true need/utility for web search, and hidden-state estimators improve decisions over self-perceived baselines.
Wildhallucinations: Evaluating long-form factuality in llms with real-world entity queries
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RISC reformulates self-consistency answer selection as a ranking task solved by a lightweight LambdaRank model with five hand-designed features, yielding better accuracy-efficiency trade-offs than majority voting on QA benchmarks.
LoVeC uses RL to train LLMs to output verbalized numerical confidence scores for statements in long-form text, achieving better calibration than self-consistency baselines on QA datasets while being 20x faster.
Per-Entity Bias Mapping claims aggregate visibility metrics fail because large brands exhibit higher fabricated citation rates than smaller ones in AI responses, attributed to the Brand Hallucination Paradox.
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To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling
A normative-descriptive framework shows LLMs' tool-calling perceptions misalign with true need/utility for web search, and hidden-state estimators improve decisions over self-perceived baselines.