--- title: "Out-of-Domain Finetuning to Bootstrap Hallucination Detection" url: https://daily.dev/posts/out-of-domain-finetuning-to-bootstrap-hallucination-detection-ngs7ewldc source_url: https://eugeneyan.com/writing/finetuning type: article source: "Eugene Yan" published: 2026-05-31T07:40:22.831Z updated: 2026-05-31T08:22:15.383Z tags: ["python", "llm"] reading_time: 10 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Out-of-Domain Finetuning to Bootstrap Hallucination Detection **[Eugene Yan](https://daily.dev/sources/eugeneyan)** · 10 min read · 0 upvotes · 0 comments ## Summary Explores using out-of-domain data to bootstrap hallucination detection in LLMs. By pre-finetuning a BART-based NLI model on Wikipedia summaries (USB dataset) before finetuning on the Factual Inconsistency Benchmark (FIB) for news summaries, PR AUC improved from 0.69 to 0.85 — a 23% gain. Recall at threshold 0.8 jumped 25x (0.02 to 0.50) and precision rose 35%. The key insight: even though USB pre-finetuning alone didn't improve FIB performance, it primed the model to learn more effectively from FIB data, demonstrating that transfer learning via sequential finetuning on related open-source datasets can reduce the need for costly in-domain labeled data. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules](https://daily.dev/posts/hybrid-neuro-symbolic-fraud-detection-guiding-neural-networks-with-domain-rules-yml9wqde4) · Towards Data Science · 1 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#llm](https://daily.dev/tags/llm) [View this post on daily.dev](https://daily.dev/posts/out-of-domain-finetuning-to-bootstrap-hallucination-detection-ngs7ewldc)