LLMLagBench: Temporal Benchmark for LLMs

• LLMLagBench is a benchmarking suite that empirically assesses the temporal boundaries of LLMs by probing their factual recall of real-world events.
• It employs an automated question curation pipeline, rigorous evaluation protocols, and changepoint detection methods to compare empirical cutoffs with declared model freshness.
• The analysis of over 60 models highlights its utility in risk mitigation, guiding retrieval-augmented generation and ensuring factual reliability in sensitive domains.

LLMLagBench is a benchmarking suite designed to empirically identify the temporal boundaries, or effective knowledge cutoffs, of LLMs by systematically probing their factual knowledge of real-world events. The benchmark provides a standardized methodology for determining the earliest probable date beyond which an LLM cannot reliably answer questions about recent events, which is central for risk mitigation in time-sensitive or compliance-critical domains. LLMLagBench delivers automated question curation, answer evaluation, and changepoint detection to analyze the freshness of LLM outputs and compare these findings to declared or inferred training cutoffs (Pęzik et al., 15 Nov 2025).

1. Purpose and Motivation

LLMs are pretrained on large textual corpora, each with a distinct training cutoff date, after which subsequent information is unavailable to the model unless augmented with retrieval mechanisms. When models are queried without awareness of their knowledge lag, particularly in medicine, law, or finance, they may produce responses that combine outdated regulatory or factual content with general knowledge, leading to undesired hallucinations and factual inaccuracies. LLMLagBench addresses three interrelated goals:

• Precisely auditing LLM data freshness in high-stakes deployments.
• Triggering retrieval-augmented generation (RAG) workflows when model knowledge is stale.
• Systematically identifying and quantifying undesirable hallucinations on temporally anchored facts.

2. Benchmark Construction and Methodology

2.1 Question Curation Pipeline

The core of LLMLagBench is a rigorously curated set of temporally anchored factual questions:

• Source Corpus: Approximately 80,000 news articles (2021–2025) from 11 major news outlets (e.g., BBC, CNN, NYT).
• Event Extraction: Articles are clustered by topic per day. DeepSeek-V3 is used to extract around 8,400 candidate event questions.
• Manual Filtering: Human validators remove questions with trivial, guessable, or predictable structure (such as scheduled sports events), retaining 1,713 unpredictable Q&A pairs. Typical retained questions require specific temporal knowledge, for example, the date of a notable figure's death or the principal actor in a recent event.

2.2 Evaluation Protocol

Each LLM is queried using a standardized, single-shot prompt: “Produce a concise answer to the following question. Generate only the answer, no additional comments. Don’t speculate. If you don’t know the answer, simply write ‘I don’t know’. Question: [QUESTION]”

Responses are evaluated using DeepSeek-V3, which rates answer faithfulness to the gold truth on a 0–2 scale. The rating procedure attains a Cohen's κ of 0.81–0.83 when compared with two independent human raters across 500 questions, which establishes high inter-rater reliability.

2.3 Changepoint Analysis

Faithfulness scores are ordered by event date to form a knowledge timeline for each model. The Pruned Exact Linear Time (PELT) algorithm is applied to detect change points, identifying abrupt decreases in performance that indicate probable boundaries of model knowledge. Refusal rates are tracked in parallel to differentiate true knowledge limitation from mere instruction tuning (e.g., when a model is prompted to say "I don't know" after a claimed cutoff).

3. Metrics

LLMLagBench provides quantitative metrics for model performance and cutoff detection:

Metric	Definition/Usage	Mathematical Expression
Accuracy	Fraction of predictions rated as faithful	$$\mathrm{Accuracy} = \frac{1}{N}\sum_{i=1}^N \mathbf{1}(\hat y_i = y_i)$$
F1 Score	Precision–recall harmonic mean on faithful label	$$\mathrm{F1} = \frac{2\cdot \mathrm{Precision}\cdot \mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}}$$
Temporal Boundary Error	Absolute difference between detected and true cutoffs	$$E = \bigl\lvert T_{\mathrm{pred}} - T_{\mathrm{true}}\bigr\rvert$$

These scores are computed over the Q&A set, enabling granular cross-model comparisons as well as longitudinal auditing for a single model.

4. Experimental Framework and Results

4.1 Model Cohort and Evaluation Design

LLMLagBench evaluated over 60 LLMs, spanning:

• Closed-source APIs: GPT-4o, GPT-3.5-turbo-0125, Claude 3.5 Sonnet & Haiku, xAI Grok-4, Google Gemini, Meta Llama-3/4, among others.
• Open-weight models: Gemma 3-4B/27B, Mistral Medium, Mixtral 8x22B, Qwen2.5-Omni-7B, DeepSeek V3, GPT-OSS 120B/20B, and more.

Evaluation is single-pass, single-prompt per model per question via vendor APIs or Hugging Face endpoints, without chain-of-thought. Faithfulness scores are logged automatically; evaluator temperature is tuned to maximize inter-rater agreement.

4.2 Observed Patterns

• Performance Spread: Highest average faithfulness scores are observed for Grok-4 (1.41), Grok-3 (1.37), Kimi K2 (1.25), DeepSeek-R1 (1.14), and Gemini 2.0-Flash (1.10); lowest for Qwen2.5-Omni-7B (0.04).
• Cutoff Boundary Patterns: Single sharp changepoints (e.g., GPT-OSS 120B, empirical cutoff September 2023 versus declared July 2024); phased or partial boundaries (Claude Sonnet 4 at February 2023 and December 2024, suggesting multistage pretraining); multichangepoint structures (Mixtral 8x22B).
• Refusal and Hallucination: Models with high instruction tuning (e.g., Grok, Claude) exhibit rapid increases in refusal rates (>90%) near declared cutoffs. Open-weight small models show lower refusal rates (<13%) but an increase in hallucinated responses.
• Detection Accuracy: Median discrepancy between LLMLagBench-detected and published cutoffs is ~1–2 months; outliers include models with misdeclared boundaries (e.g., GPT-OSS 120B, declared September 2021, empirical September 2023).

5. Reliability, Auditability, and Implications

Manual assessment of 500 randomly selected Q&A pairs confirms that the automated rating procedure yields a Cohen's κ above 0.8, substantiating benchmark reliability. Comparative analysis of more than 50 models reveals that LLMLagBench cutoff estimates typically align within weeks of public disclosures and systematically surface misalignments in declared model freshness.

Key applications include:

• Automated audit of model recency before deployment.
• Real-time triggering of RAG when queries target events post-cutoff.
• Identification of models at risk for hallucinations on current events (that is, low refusals and low answer faithfulness).

A plausible implication is that LLMLagBench forms the basis for ongoing empirical monitoring and continuous compliance assurance as new LLM versions appear.

6. Limitations and Prospective Directions

Identified limitations include binary events with a narrow set of possible answers, resulting in approximately 50% chance-level guessing on some tasks and introducing noise in cutoff detection. The automated evaluator may assign high faithfulness to plausible but incorrect answers. Future work is suggested in multi-choice or structured Q/A formats to decrease the effect of guessing, in extending the benchmark to temporal reasoning (not just recall), and in refining PELT parameters for more sensitive changepoint identification. Regional, domain-specific expansions (e.g., local news, regulations) and continuous online benchmarking are viable extensions (Pęzik et al., 15 Nov 2025).

7. Broader Context and Impact

LLMLagBench establishes an empirical, fully automated pipeline for detecting the temporal knowledge cutoffs of LLMs. This resource is directly applicable for model auditing, adaptive retrieval triggering, and risk analysis in regulated or fast-moving domains. Its design supports reproducibility via standardized prompts, robust inter-rater agreement, and comparison to public declarations. As LLM deployment accelerates in critical fields, LLMLagBench offers a principled framework for verifying model currency, guiding retrieval strategies, and prioritizing updates in knowledge-sensitive workflows (Pęzik et al., 15 Nov 2025).