CLApr 24

Evaluating Temporal Consistency in Multi-Turn Language Models

arXiv:2604.2305191.81 citationsHas Code
Predicted impact top 25% in CL · last 90 daysOriginality Incremental advance
AI Analysis

For developers of conversational AI systems, this work identifies a critical gap in temporal reasoning that undermines coherent multi-turn interactions, highlighting a need for improved temporal consistency.

The paper introduces ChronoScope, a benchmark with over one million question chains to evaluate temporal scope stability in multi-turn language models. It finds that models frequently fail to maintain temporal context across turns, with errors increasing over longer interactions, even when they have correct factual knowledge.

Language models are increasingly deployed in interactive settings where users reason about facts over time rather than in isolation. In such scenarios, correct behavior requires models to maintain and update implicit temporal assumptions established earlier in a conversation. We study this challenge through the lens of temporal scope stability: the ability to preserve, override, or transfer time-scoped factual context across dialogue turns. We introduce ChronoScope, a large-scale diagnostic benchmark designed to isolate temporal scope behavior in controlled multi-turn interactions, comprising over one million deterministically generated question chains grounded in Wikidata. ChronoScope evaluates whether models can correctly retain inferred temporal scope when follow-up questions omit explicit time references, spanning implicit carryover, explicit scope switching, cross-entity transfer, and longer temporal trajectories. Through extensive evaluation of state-of-the-art language models, we find that temporal scope stability is frequently violated in controlled multi-turn settings, with models often drifting toward present-day assumptions despite correct underlying knowledge. These failures intensify with interaction length and persist even under oracle context conditions, revealing a gap between single-turn factual accuracy and coherent temporal reasoning under sequential interaction. We make our dataset and evaluation suite publicly available at https://github.com/yashkumaratri/ChronoScope

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