Temporal Referential Consistency: Do LLMs Favor Sequences Over Absolute Time References?

AI summary

Overview

• Research area: Natural Language Processing, specifically temporal reasoning and consistency in large language models (LLMs).
• Technical level: Intermediate. The paper assumes familiarity with LLM prompting strategies, instruction tuning, and evaluation metrics, but its core argument (models answer differently when a date is swapped for an event) is easy to grasp.
• Scope in one sentence: The paper introduces a new benchmark called temporal referential consistency, releases a multilingual dataset (TEMP-ReCon) built from the TEMPREASON dataset, shows that a wide range of open- and closed-source LLMs fail at it, and proposes a fine-tuning method (UnTRaP) that improves it.

What This Paper Is About

LLMs are increasingly used as knowledge sources in time-sensitive fields such as law, healthcare, and finance, so they should give the same answer whether a question refers to a date (for example, "right before January 1949") or to an event (for example, "right before Concordia Seminary"). The paper shows that they often do not, and formalizes this gap as "temporal referential consistency." The goal is to measure this behavior across models and languages, then propose a training method that fixes it.

Key Contributions

1. A new benchmark and resource. The authors formalize temporal referential consistency and build TEMP-ReCon, a multilingual dataset covering English, French, and Romanian, derived from the event-event (L3) category of TEMPREASON. It contains 13,014 train, 4,437 dev, and 4,426 test queries.
2. An empirical diagnosis. Across open-source models (LLaMA3.1-8B, Mistral-v1, Vicuna-7b-v1.5, Bloomz-7b1) and a closed-source model (Gemini 2.5 Pro), the paper reports uniformly weak temporal referential consistency and finds that models handle chronological (event) references better than absolute (date) references.
3. A method for improvement. The authors propose UnTRaP (Unifying Temporal Reasoning Pathways), which aligns event-oriented and time-oriented reasoning paths through supervised instruction tuning.
4. Multilingual and cross-model validation. An extended variant, UnTRaP*, is trained on multiple languages, and UnTRaP is also tested on LLaMA2-13B-Chat to check generalization across model sizes and architectures.

Main Findings

• All tested models perform poorly. On the English test set, metrics range from -0.34% to -4.34% for EM-based temporal referential factual deviation, 11.32% to 38.84% for temporal referential consistency, and 0.23% to 10.42% for temporally referential consistent factuality.
• Chronological references beat absolute references. Models consistently perform better on event-based (chronological) queries than on date-based (absolute) queries, which is the source of the inconsistency.
• Gemini 2.5 Pro is better but still suboptimal. It substantially outperforms the open-source models but still shows weakness on temporal references.
• Prompting strategy matters. Semantic Chain-of-Thought improves temporally referential consistent factuality but simultaneously widens the gap in EM-based temporal factuality.
• No clear link to language resource richness. Across English, French, and Romanian, scores range from -0.04% to -4.34% (EM deviation), 8.65% to 26.77% (temporal referential consistency), and 0.77% to 10.42% (consistent factuality); the paper attributes the lack of correlation to overall very poor performance across languages.
• UnTRaP outperforms baselines. It beats CoTSeLF by 16.69 and 8.97 percentage points on temporal referential consistency and temporally referential consistent factuality, and beats TSRL by 9.06 and 5.47 percentage points on the same two metrics.
• Multilingual UnTRaP* generalizes. It improves over the Semantic CoT baseline in every language and metric, with mean gains of 2.38, 1.33, 14.91, and 4.75 across the four metrics. On Romanian it reaches a temporal referential consistency score of 36.42 versus 20.40 for Semantic CoT; on English it reaches 39.36.
• It scales to another base model. On LLaMA2-13B-Chat, temporal referential consistency rises to 43.81, a gain of 30.71 over the corresponding baseline figure of 13.1.
• Unilateral fine-tuning hurts. Fine-tuning only on absolute temporal references with the event-oriented path causes a decline of 29.64 percentage points in temporal referential consistency relative to UnTRaP.
• Entity types behave unevenly. Improvements of 33.44 and 30.86 percentage points appear for the "position" entity type, but the "team" entity type shows no significant gain. The correlation between entity-type distribution and the model's temporal referential consistency is -0.15, while the correlation between CoTSeLF and the proposed model's consistent factuality is 0.95.

Methodology in Plain English

The authors start from TEMPREASON, an existing temporal reasoning dataset, and take only its event-event (L3) instances because those pair events with the time periods attached to them. For each item they build two versions of the same question: one referring to a date, and one referring to the event. They then construct two reasoning paths that a model could follow to reach the answer — a time-oriented path for the event-referenced question and an event-oriented path for the date-referenced question — and store both alongside the question pair and the correct answer. Each TEMP-ReCon instance therefore has five fields: absolute reference query, chronological reference query, correct answer, time-oriented reasoning pathway, and event-oriented reasoning pathway.

The dataset is extended to French and Romanian via automatic translation with T5. Translation quality is checked with a mean Translation Success Rate of 99.4%, a mean Back-Translation Accuracy of 71.49% and 45.49% under BERTScore and ChrF++, and inter-translator agreement with NLLB-200 of 93.75 and 62.48 under BERTScore and BLEU-3.

Evaluation relies on three metrics extended from prior work: temporal referential factual deviation (the gap in factuality between the two query types, using Exact Match and F1), temporal referential consistency (whether the model's two answers are identical), and temporally referential consistent factuality (whether the answers are both identical and correct). UnTRaP is implemented on LLaMA3.1-8B with LoRA-based supervised instruction tuning, training the model to produce the answer together with the matching reasoning path for each reference type, with the intent that the two pathways become aligned in the model's internal representations. A multilingual variant, UnTRaP*, is trained on instances across languages, and it is compared against ICL, Semantic ICL, Semantic CoT, SFT, SFT + TSRL, and CoTSeLF.

Why This Matters

• Impact on research: The paper reframes temporal consistency beyond paraphrase-level rewording, arguing that consistency must hold across different ways of referring to time itself. It supplies a dataset and metric definitions that other researchers can reuse, and it shows a training-time approach to reasoning-path alignment that reduces inconsistency rather than merely measuring it.
• Real-world applications:
  • Legal research, where contracts and statutes are referenced both by date and by named events.
  • Healthcare, where disease progression is described relative to dates and to clinical events.
  • Finance, where market information is queried by reporting period or by a named event.
  • Historical text analysis and timeline summarization, where events and dates are used interchangeably.
• Industry relevance: Any system that answers time-sensitive questions from an LLM, or that must return the same answer regardless of how a user phrases a date, benefits directly. The paper also shows a concrete failure mode for teams that fine-tune only on one reference style, and it reports translation-quality figures that matter for multilingual deployments.

Future Directions

• Entity-specific adjustments to UnTRaP. The paper explicitly notes the need for this, since gains are large for the "position" entity type but negligible for "team" entities.
• Extending to closed-source models. The authors state that UnTRaP requires access to model parameters, making closed-source LLMs potentially infeasible to improve this way.
• Scaling to larger models. Computational resource constraints limited experiments to 8B and 13B parameter models.
• Expanding beyond the source data's constraints. TEMP-ReCon does not correct the source dataset's anchoring on a single entity type (person names) and its limited entity range, and the authors note that temporal referential consistency is only one aspect of temporal consistency, requiring integration with other methodologies for a complete evaluation.

Target Audience

This paper is most useful for NLP researchers working on temporal reasoning, consistency, and evaluation benchmarks; practitioners building time-sensitive question-answering or retrieval systems on LLMs; and engineers doing multilingual instruction tuning who need to know how prompt strategy and reference phrasing affect answer stability. Readers looking for a quick introduction to why LLM answers vary when dates become events will also find the framing approachable, though the metric tables and fine-tuning setup assume some familiarity with LLM evaluation.

Authors’ abstract

The increasing acceptance of large language models (LLMs) as an alternative to knowledge sources marks a significant paradigm shift across various domains, including time-sensitive fields such as law, healthcare, and finance. To fulfill this expanded role, LLMs must not only be factually accurate but also demonstrate consistency across temporal dimensions, necessitating robust temporal reasoning capabilities. Despite this critical requirement, efforts to ensure temporal consistency in LLMs remain scarce including noticeable absence of endeavors aimed at evaluating or augmenting LLMs across temporal references in time-sensitive inquiries. In this paper, we seek to address this gap by introducing a novel benchmark entitled temporal referential consistency, accompanied by a resource TEMP-ReCon designed to benchmark a wide range of both open-source and closed-source LLMs with various linguistic contexts characterized by differing resource richness (including English, French, and Romanian). The findings emphasis that LLMs do exhibit insufficient temporal referent consistency. To address this, we propose \newmodel, a reasoning path alignment-based model that aims to enhance the temporal referential consistency of LLMs. Our empirical experiments substantiate the efficacy of UnTRaP compared to several baseline models.