The Moving Target: A Longitudinal Audit of Trust-Benchmark Score Drift Across Open-Source Chat LLM Release Lines
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Abstract (arXiv)
Trust-benchmark scores reported on a chat-LLM release line are often carried across several checkpoints of the same line, as if the underlying model had not shifted between releases. We test that assumption. We audit four open-source release lines (Yi, Qwen, Mistral, and Gemma) at three successive public generations each. Each checkpoint is scored on a fixed 200-item basket of five chat-evaluation benchmarks: TruthfulQA, BBQ, ToxiGen, CrowS-Pairs, and XSTest, under three prompt templates. Four of the five benchmark variants are non-canonical, and two of those are synthetic proxies. The mean absolute adjacent-generation Score Drift Rate is several times the mean of an independence-based count-level reference null. It stays in the same band when we drop a benchmark, drop a release line, switch to strict scoring, or restrict to constant-parameter-size transitions. Within this audited setup, a quoted trust score should be treated as checkpoint-bound. It should be re-measured on each materially new release rather than carried forward. Closed APIs, larger models, canonical-protocol scores, and benchmark-item-subset uncertainty are out of scope.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 27 references that are in the map. First posted on arXiv 1 Jul 2026.
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24 of 27 references.
References · 24
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- 2024Qwen2 Technical Reportcited by 2,076
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- 2024Gemma: Open Models Based on Gemini Research and Technologycited by 889
- 2024Yi: Open Foundation Models by 01.AIcited by 706
- 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 703
12 more
- 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
- 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 505
- 2023XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Modelscited by 477
- 2021Dynabench: Rethinking Benchmarking in NLPcited by 447
- 2022ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detectioncited by 419
- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
- 2021AI and the Everything in the Whole Wide World Benchmarkcited by 340
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- 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 126
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Citing papers per year: 2026: 2.
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