UG-TTT adds epistemic uncertainty measured by adapter disagreement as an exploration bonus in RL for LLMs, raising maximum reward and diversity on scientific discovery benchmarks.
LoRA ensembles for large language model fine-tuning.arXiv preprint arXiv:2310.00035
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BaLoRA is a Bayesian LoRA variant with input-adaptive noise that improves accuracy over standard LoRA and supplies well-calibrated uncertainty estimates on language, vision, and scientific prediction tasks.
Cross-model semantic disagreement adds an epistemic uncertainty term that improves total uncertainty estimation over self-consistency alone, helping flag confident errors in LLMs.
Succinct Model Difference Proofs certify that a neural-network update stays inside a policy-defined drift class using zero-knowledge proofs whose cost depends only on the drift structure.
PoLAR-VBLL combines orthogonalized low-rank adapters with variational Bayesian last-layer inference to enable scalable, well-calibrated uncertainty quantification in fine-tuned LLMs.
Fine-tuned small language models trained on a synthetic Windows event log dataset with remediation steps outperform larger models in issue detection and solution generation with lower computational cost.
citing papers explorer
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Epistemic Uncertainty for Test-Time Discovery
UG-TTT adds epistemic uncertainty measured by adapter disagreement as an exploration bonus in RL for LLMs, raising maximum reward and diversity on scientific discovery benchmarks.
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BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models
BaLoRA is a Bayesian LoRA variant with input-adaptive noise that improves accuracy over standard LoRA and supplies well-calibrated uncertainty estimates on language, vision, and scientific prediction tasks.
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Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
Cross-model semantic disagreement adds an epistemic uncertainty term that improves total uncertainty estimation over self-consistency alone, helping flag confident errors in LLMs.
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Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates
Succinct Model Difference Proofs certify that a neural-network update stays inside a policy-defined drift class using zero-knowledge proofs whose cost depends only on the drift structure.
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Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters
PoLAR-VBLL combines orthogonalized low-rank adapters with variational Bayesian last-layer inference to enable scalable, well-calibrated uncertainty quantification in fine-tuned LLMs.
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Fine-Tuning Small Language Models for Solution-Oriented Windows Event Log Analysis
Fine-tuned small language models trained on a synthetic Windows event log dataset with remediation steps outperform larger models in issue detection and solution generation with lower computational cost.