Judge upgrades are not interchangeable: only Qwen3 1.7B→4B yields robust adjacent gains, MiniMax adjacent releases do not, and stronger judges reduce but do not remove bias or correlated jury errors.
The TechQA Dataset
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abstract
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain. The TechQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size -- 600 training, 310 dev, and 490 evaluation question/answer pairs -- thus reflecting the cost of creating large labeled datasets with actual data. Consequently, TechQA is meant to stimulate research in domain adaptation rather than being a resource to build QA systems from scratch. The dataset was obtained by crawling the IBM Developer and IBM DeveloperWorks forums for questions with accepted answers that appear in a published IBM Technote---a technical document that addresses a specific technical issue. We also release a collection of the 801,998 publicly available Technotes as of April 4, 2019 as a companion resource that might be used for pretraining, to learn representations of the IT domain language.
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cs.CL 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability
Judge upgrades are not interchangeable: only Qwen3 1.7B→4B yields robust adjacent gains, MiniMax adjacent releases do not, and stronger judges reduce but do not remove bias or correlated jury errors.