aiqbench analysis
IBBench-Light introduces a rigorous paired evaluation framework that exposes significant gaps in model reliability when handling external directives versus reading tasks, challenging the sufficiency of standard marginal accuracy metrics. By incorporating detailed stopping policies and literal-target audits, this research provides critical insights into the conditional task success rates of quantized instruction models like Qwen and Phi. The study highlights how minor configuration changes, such as end-of-sequence token sets, can drastically alter exact paired success rates, offering a more granular view of model behavior under strict output contracts.
IBBench-Light: A Paired Evaluation of Task-Conditioned Responses to External Directives
arXiv:2609.13725v1 Announce Type: new Abstract: An external record may contain a procedure to apply or text to read, depending on the user's request. IBBench-Light tests both uses against the same record. Twelve semantic bases yield 144 matched pairs per model; four quantized instruction models produced 1,152 archived greedy responses. Paired exact-contract accuracy (PECA) requires both members to satisfy their output contracts. Qwen succeeds on 132 execute and 109 process prompts, but only 97 complete pairs, showing what marginal averages omit. We audit literal-target exposure and case normalization, then add 1,722 logged CPU generations to test directive-absent controls, twelve additional semantic bases, within-base wording changes, and generation stopping. In the pinned Phi rerun, changing the end-of-sequence (EOS) set changes exact paired success from 0/144 to 62/144. A bounded IHEval comparison uses the same SmolLM2 checkpoint and output budget while preserving its published instruction roles and scorer. The benchmark measures conditional task and output-contract success. Its task margins and paired count need to be read together with the stopping policy.
Abstract:An external record may contain a procedure to apply or text to read, depending on the user's request. IBBench-Light tests both uses against the same record. Twelve semantic bases yield 144 matched pairs per model; four quantized instruction models produced 1,152 archived greedy responses. Paired exact-contract accuracy (PECA) requires both members to satisfy their output contracts. Qwen succeeds on 132 execute and 109 process prompts, but only 97 complete pairs, showing what marginal averages omit. We audit literal-target exposure and case normalization, then add 1,722 logged CPU generations to test directive-absent controls, twelve additional semantic bases, within-base wording changes, and generation stopping. In the pinned Phi rerun, changing the end-of-sequence (EOS) set changes exact paired success from 0/144 to 62/144. A bounded IHEval comparison uses the same SmolLM2 checkpoint and output budget while preserving its published instruction roles and scorer. The benchmark measures conditional task and output-contract success. Its task margins and paired count need to be read together with the stopping policy.
Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
arXiv:2609.13725 [cs.AI]
(or arXiv:2609.13725v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.13725 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 262 words
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