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hallbayes https://github.com/leochlon/hallbayes is an interesting project by Leon Chlon (Hassana Labs) for checking for hallucination risk before text generation and it uses a powerful approach to decide if an LLM is confident enough to answer (or not).
https://arxiv.org/html/2509.11208v1
Predictable Compression Failures: Why Language Models Actually Hallucinate (2509.11208)
I've just integrated the hallbayes library into my
Ran a small test on 10 samples on google/boolq with a 4B Qwen Instruct model Qwen/Qwen3-4B-Instruct-2507.
The output dataset now contains a
Test w/ hallucination flags: ethicalabs/google-boolq-hallbayes-test-qwen3-4b-2507
Implementation MRs:
https://github.com/leochlon/hallbayes/pull/16
https://github.com/ethicalabs-ai/completionist/pull/11
https://arxiv.org/html/2509.11208v1
Predictable Compression Failures: Why Language Models Actually Hallucinate (2509.11208)
I've just integrated the hallbayes library into my
completionist (synthetic dataset generation CLI tool) project to do exactly that, adding a new quality control layer to synthetic data generation.Ran a small test on 10 samples on google/boolq with a 4B Qwen Instruct model Qwen/Qwen3-4B-Instruct-2507.
The output dataset now contains a
hallucination_info column, flagging each sample with detailed metrics. The inference server is LM Studio, running on a Macbook Air M4 16GBTest w/ hallucination flags: ethicalabs/google-boolq-hallbayes-test-qwen3-4b-2507
Implementation MRs:
https://github.com/leochlon/hallbayes/pull/16
https://github.com/ethicalabs-ai/completionist/pull/11