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New TASC benchmark reveals LLM assistants struggle with time-sensitive safety

By PulseAugur Editorial · [1 sources] ·

A new benchmark called TASC has been developed to evaluate the time-sensitive safety awareness of conversational AI assistants. The benchmark includes over 3,400 inputs and 33 scenarios to test if LLMs can recognize and adjust their advice based on time-dependent constraints, even if users don't explicitly mention them later in a conversation. Initial testing across 13 models revealed that assistants only accounted for these constraints in about 30.5% of relevant cases, and notably, failed to apply them in 62.1% of instances despite correctly calculating elapsed time. AI

IMPACT This research highlights a critical gap in LLM safety, indicating a need for improved temporal reasoning and application in conversational agents.

RANK_REASON The cluster contains a research paper introducing a new 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 TASC benchmark reveals LLM assistants struggle with time-sensitive safety

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27 / 100
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The cluster contains a research paper introducing a new benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, safety
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Peidi Dong, Yunze Xiao, Yu-Lin Tsai, Yichen Zheng, Heyuan Huang, Weihao Xuan, Mark Dredze, Jen-tse Huang ·

    Before You Continue: Evaluating Time-Sensitive Safety Awareness in Conversational Assistants

    arXiv:2611.06839v1 Announce Type: new Abstract: Large language model (LLM) assistants increasingly help users with everyday activities that involve time-dependent constraints. However, users may not mention these constraints in later requests, requiring assistants to recognize an…

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