Verifying LLM Output, Sorta, Kinda (theaiunderwriter.substack.com)

Large language models (LLMs) are powerful at generating answers but fundamentally lack the ability to verify the factual correctness of their outputs, leading to issues like hallucinations—confident but wrong answers. This presents a major challenge for deploying LLMs in critical applications such as automated data extraction, where accuracy and verifiability are paramount. The article highlights that while humans can cross-check facts against external references, LLMs operate without intrinsic epistemic certainty, relying instead on probabilistic token generation that doesn’t guarantee truthfulness. To address this verification gap, researchers employ a patchwork of imperfect methods that serve as proxies for output reliability rather than definitive truth checks. Techniques include self-consistency checks (running prompts multiple times to gauge output stability), cross-model validation (comparing answers from different LLMs), assessing token-level probabilities (logprobs), adversarial prompt testing, and grounding responses using retrieval-augmented generation (RAG) linked to external knowledge bases. Each method offers partial signals—entropic variability, semantic alignment, or consensus—that, when combined into ensembles, improve confidence but still fall short of foolproof verification. The piece underscores the nuance between a model’s internal confidence (distributional certainty) versus real-world correctness (epistemic certainty), noting that current tools provide only a “Veracity Proxy Score” rather than absolute truth. The significance of this discussion lies in its call for layered, combined approaches to output verification as a practical interim solution while research continues on understanding LLM decision processes at a granular level. Until true verification emerges, developers must rely on ensemble strategies that aggregate weak signals to reduce errors without hindering throughput—highlighting both the promise and present limitations of LLMs in high-stakes, regulated environments.