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New dataset and benchmark test LLM abstention in Arctic science

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ArcticQA, a new dataset comprising 194 multiple-choice questions related to Arctic science, designed to evaluate the abstention capabilities of large language models. Alongside this, they developed ArcticAbstain, a benchmark that specifically tests how well models abstain when no correct answer is provided or when the correct answer is replaced by a distractor. Eight models from the Gemini, Claude, and ChatGPT families were tested, revealing significant differences in their abstention rates and responsiveness to answer availability. AI

IMPACT Highlights the need for LLMs to accurately abstain from answering when information is unavailable, a critical capability for reliable scientific reasoning.

RANK_REASON The cluster describes a new academic paper introducing a dataset and benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset and benchmark test LLM abstention in Arctic science

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The cluster describes a new academic paper introducing a dataset and benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Benjamin Wilcox, Dawei Gao, Pradeeban Kathiravelu, Douglas Causey, Kewei Sha, Yunhe Feng ·

    Arctic Questions, Missing Answers: A Dataset and Benchmark for LLM Abstention in Arctic Science

    arXiv:2610.09446v1 Announce Type: new Abstract: Large language models (LLMs) should abstain from scientific multiple-choice questions when no option is valid, but frequent abstention alone does not demonstrate sensitivity to answer availability. We introduce ArcticQA, a dataset o…

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