BiG-SURE - Bipartite Graph for Semantic Uncertainty and Reliability Estimation of LLMs
Reliable uncertainty estimation is a crucial requirement for deploying large language models (LLMs) and vision-language models (VLMs) in safety-critical settings, especially when the model parameters are not accessible (black-box). We propose BiG-SURE, an uncertainty estimator based on cross-temperature semantic agreement. The method samples low-temperature responses as stable semantic anchors and high-temperature responses as probes under meaning-preserving input transformations. It then constructs an anchor-probe Bipartite Graph (BiG) using NLI-based entailment scores and defines confidence
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- PossiblePossibly related (embedding) · 49%Large language models as uncertainty-calibrated optimizers for experimental discovery →
- FuzzySimilar title/name (fuzzy) · 59%tirth8205/code-review-graph →
“Fuzzy title match (0.73): “BiG-SURE - Bipartite Graph for Semantic Uncertainty and Reli” ≈ “tirth8205/code-review-graph””
- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “BiG-SURE - Bipartite Graph for Semantic Uncertainty and Reli” ≈ “microsoft/semantic-kernel””
- LinkedLinked via arxiv author · 85%Debarpan Bhattacharya →
“BiG-SURE - Bipartite Graph for Semantic Uncertainty and Reliability Estimation of LLMs”
- LinkedLinked via arxiv author · 85%Malay Phadke →
“BiG-SURE - Bipartite Graph for Semantic Uncertainty and Reliability Estimation of LLMs”
- LinkedLinked via arxiv author · 85%Sriram Ganapathy →
“BiG-SURE - Bipartite Graph for Semantic Uncertainty and Reliability Estimation of LLMs”