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In Case You Missed It: ARC 'Challenge' Is Not That Challenging

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arxiv 2412.17758 v1 pith:DPTGO2QX submitted 2024-12-23 cs.CL cs.AI

classification cs.CLcs.AI
keywords evaluationchallengeaccuratelyacknowledgedactualalthoughanswerappears
verification ladder T0 review T1 audit T2 compute T3 formal
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ARC Challenge appears more difficult than ARC Easy for modern LLMs primarily due to an evaluation setup that prevents direct comparison of answer choices rather than inherent complexity. Although some researchers have quietly shifted to a more appropriate scheme over the last year, the implications of this change have yet to be widely acknowledged. We highlight this overlooked shift, show how similar evaluation practices falsely imply reasoning deficits in other benchmarks, and demonstrate that fairer methods dramatically reduce performance gaps (e.g. on SIQA) and even yield superhuman results (OpenBookQA). In doing so, we reveal how evaluation shapes perceived difficulty and offer guidelines to ensure that multiple-choice evaluations accurately reflect actual model capabilities.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Self-Route uses hidden-layer representations from a brief pre-inference plan to route each question to either short or long chain-of-thought, cutting tokens by 30-55% with under 2% accuracy loss.

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