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This paper investigates whether label-free strategies for multiple-choice benchmarks can remove option-order sensitivity in large language models, finding that neither two-stage prompting nor independent hypothesis scoring reliably improves accuracy.
The paper tests label-free strategies to reduce positional bias in multiple-choice benchmarks but finds they do not reliably improve accuracy or reduce bias. Only showing all options with an LLM matcher preserves baseline performance, while cyclic permutation sometimes helps.
Introduces CDR-Bench, a benchmark with 3,462 tasks to evaluate LLMs' ability to faithfully execute compositional, order-sensitive data refinement recipes. Experiments on 10+ LLMs reveal significant performance degradation in compositional and order-sensitive settings, highlighting a lack of procedural faithfulness.
A paper accepted at ICML 2026 introduces predictable hallucination via an information-budget abstention gate, and releases ntkMirror, a training-free open-weight implementation that reduces hallucination by abstaining when information is insufficient, achieving 0.0–0.7% hallucination at ~24% abstention.