Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916476643966976 |
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| author | Naik, Ninad |
| author_facet | Naik, Ninad |
| contents | Large Language Models (LLMs) have shown significant advances in text generation but often lack the reliability needed for autonomous deployment in high-stakes domains like healthcare, law, and finance. Existing approaches rely on external knowledge or human oversight, limiting scalability. We introduce a novel framework that repurposes ensemble methods for content validation through model consensus. In tests across 78 complex cases requiring factual accuracy and causal consistency, our framework improved precision from 73.1% to 93.9% with two models (95% CI: 83.5%-97.9%) and to 95.6% with three models (95% CI: 85.2%-98.8%). Statistical analysis indicates strong inter-model agreement ($κ$ > 0.76) while preserving sufficient independence to catch errors through disagreement. We outline a clear pathway to further enhance precision with additional validators and refinements. Although the current approach is constrained by multiple-choice format requirements and processing latency, it offers immediate value for enabling reliable autonomous AI systems in critical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06535 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability Naik, Ninad Artificial Intelligence Computation and Language Machine Learning Large Language Models (LLMs) have shown significant advances in text generation but often lack the reliability needed for autonomous deployment in high-stakes domains like healthcare, law, and finance. Existing approaches rely on external knowledge or human oversight, limiting scalability. We introduce a novel framework that repurposes ensemble methods for content validation through model consensus. In tests across 78 complex cases requiring factual accuracy and causal consistency, our framework improved precision from 73.1% to 93.9% with two models (95% CI: 83.5%-97.9%) and to 95.6% with three models (95% CI: 85.2%-98.8%). Statistical analysis indicates strong inter-model agreement ($κ$ > 0.76) while preserving sufficient independence to catch errors through disagreement. We outline a clear pathway to further enhance precision with additional validators and refinements. Although the current approach is constrained by multiple-choice format requirements and processing latency, it offers immediate value for enabling reliable autonomous AI systems in critical applications. |
| title | Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2411.06535 |