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ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains
Yein Park, Chanwoong Yoon, Jungwoo Park, Donghyeon Lee, Minbyul Jeong, Jaewoo Kang
TL;DR
LLMs need evaluation beyond isolated timestamps because chronological knowledge can become outdated, incomplete, or only partially recalled. The paper introduces ChroKnowBench and ChroKnowledge, then uses ChroKnowPrompt to traverse neighboring time spans; recall improves across model types, especially for unchanged objects, while changed historical objects remain difficult.
Problem
Existing temporal evaluations often focus on single timestamps, limiting assessment of knowledge evolution across domains and risking outdated or incomplete information.
Method
The paper introduces ChroKnowBench and ChroKnowledge, then applies ChroKnowPrompt to traverse adjacent time spans when eliciting chronological knowledge.
Results
ChroKnowPrompt improves recall across proprietary and open-source LLMs, with average gains of 11.5% in biomedical and 2.8% in general domains for unchanged objects.
Takeaways & Limitations
Chronological prompting reduces Partial Known cases and increases Known cases, demonstrating a practical way to elicit temporal knowledge without extensive retraining.
Takeaways & Limitations
Prompting remains limited for changed objects in dynamic datasets, reaching only 10–30% of unchanged-object performance and averaging 0.4 in general domains.
Abstract
from arXiv · showhide
Large language models (LLMs) have brought significant changes to many aspects of our lives. However, assessing and ensuring their chronological knowledge remains challenging. Existing approaches fall short in addressing the temporal adaptability of knowledge, often relying on a fixed time-point view. To overcome this, we introduce ChroKnowBench, a benchmark dataset designed to evaluate chronologically accumulated knowledge across three key aspects: multiple domains, time dependency, temporal state. Our benchmark distinguishes between knowledge that evolves (e.g., personal history, scientific discoveries, amended laws) and knowledge that remain constant (e.g., mathematical truths, commonsense facts). Building on this benchmark, we present ChroKnowledge (Chronological Categorization of Knowledge), a novel sampling-based framework for evaluating LLMs' non-parametric chronological knowledge. Our evaluation led to the following observations: (1) The ability of eliciting temporal knowledge varies depending on the data format that model was trained on. (2) LLMs partially recall knowledge or show a cut-off at temporal boundaries rather than recalling all aspects of knowledge correctly. Thus, we apply our ChroKnowPrompt, an in-depth prompting to elicit chronological knowledge by traversing step-by-step through the surrounding time spans. We observe that it successfully recalls objects across both open-source and proprietary LLMs, demonstrating versatility, though it faces challenges with dynamic datasets and unstructured formats.
1 INTRODUCTION
The paper argues that evaluating LLM knowledge at isolated timestamps misses how knowledge evolves, and introduces a benchmark and framework for chronological evaluation across domains and temporal states.
- Existing temporal approaches often use single timestamps, leaving LLMs vulnerable to outdated or incomplete information about evolving facts.
- ChroKnowBench evaluates chronologically accumulated knowledge across multiple domains, time dependency, and dynamic or static temporal states.It distinguishes evolving object attributes from invariant knowledge such as mathematical truths and commonsense facts.
- ChroKnowledge evaluates and enhances non-parametric chronological knowledge in open-source and proprietary LLMs.Its initial analysis finds steadier performance for time-invariant knowledge and domain-dependent variation for time-variant knowledge.
- ChroKnowPrompt traverses adjacent time spans to address partial recall and temporal boundaries during knowledge elicitation.
- 11.5% and 2.8% average recall improvements occur in biomedical and general domains, respectively, shifting unchanged objects from Partial Known to Known.The approach applies directly to proprietary and open-source models without extensive retraining.
2 PRELIMINARIES
The preliminaries extend sampled knowledge categorization with timestamps and use three answer formats to expose differences in how LLMs express temporal knowledge.
- 2.1 KNOWLEDGE CATEGORIZATION WITH A TEMPORAL COMPONENT: Sampling-based categorization labels temporal responses as Correct, Partial Correct, or Incorrect by comparing sampled answers with an answer set.Five few-shot exemplar combinations and temperatures τ = 0 and τ > 0 support repeated assessment.
- 2.1 KNOWLEDGE CATEGORIZATION WITH A TEMPORAL COMPONENT: Adding a time attribute to subject-relation-object triples enables evaluation across timestamps but still captures knowledge only at specific points.
- 2.2 ELICITING KNOWLEDGE USING DIVERSE TEMPLATES: The benchmark elicits knowledge with Generation, four-option MCQA, and True/False templates because models prefer different formats.Generation responses use fuzzy matching against predefined labels.
- 2.2 ELICITING KNOWLEDGE USING DIVERSE TEMPLATES: Table 2 organizes benchmark instances by time variant or invariant knowledge and by dynamic or static temporal state across five domains.
3 CHROKNOWBENCH: CONSTRUCTING A BENCHMARK DATASET
ChroKnowBench is constructed as a timestamped, object-focused benchmark spanning time-sensitive and invariant knowledge across five domains and two temporal states.
- 3 CHROKNOWBENCH: CONSTRUCTING A BENCHMARK DATASET: ChroKnowBench spans general, biomedical, legal, commonsense, and mathematical domains, separating time-variant from time-invariant knowledge and dynamic from static states.
- 3.1 TASK DEFINITION: The task tracks changes in object attributes while keeping the subject and relation fixed, enabling fine-grained temporal transformations.Missing yearly objects are filled using available objects across the intervening period.
- 3.2 DATASET GENERATION: Annual knowledge sources are normalized, deduplicated, and organized into object pools associated with yearly time frames.Dynamic instances contain changes over the period, whereas static instances have change potential but no observed change.
- 3.2 DATASET GENERATION: The general-domain performance analysis compares dynamic and static knowledge and shows stronger intermediate-year performance with declines in recent years.
- 3.3 TIME VARIANT & INVARIANT KNOWLEDGE: Time-variant data come from Wikidata, UMLS, and the Code of Federal Regulations, while time-invariant data come from commonsense and mathematics datasets.The legal data use a QA-like format derived from unstructured XML.
4 EXPERIMENTAL SETUP
The experiments evaluate nine open-source and two proprietary instruction or chat LLMs, with biomedical results showing lower variability than general-domain results and a recent-year decline.
- The study evaluates nine open-source and two proprietary LLMs using instruction-tuned or chat versions.The models differ in corpus coverage, enabling analysis of chronological knowledge trends.
- Biomedical dynamic and static datasets show lower variability than the general domain, but both decline between 2022 and 2023.
- Biomedical generation performance declines over time, whereas MCQA and True/False performance remains strong.
5 CHROKNOWLEDGE: CHRONOLOGICAL CATEGORIZATION OF KNOWLEDGE
ChroKnowledge evaluates chronological knowledge across time, templates, and domains, finding that temporal performance varies with knowledge dynamics and elicitation format. Models decline most on recent dynamic knowledge, while domain-specific stability and structured templates moderate this behavior.
- 5 CHROKNOWLEDGE: CHRONOLOGICAL CATEGORIZATION OF KNOWLEDGE: The framework samples each knowledge item five times across dynamic and static datasets to compare temporal-, template-, and domain-wise behavior.The main analysis covers general, biomedical, and legal time-variant domains, with time-invariant results reported separately.
- 5.1 RESULTS OF REPRESENTING KNOWLEDGE FROM LARGE LANGUAGE MODELS: Models show a steeper decline on recent knowledge in dynamic datasets, whereas static datasets are more stable but weaker and less temporally sensitive.The trend is linked to pretraining cutoffs and motivates frequent updates for dynamic knowledge.
- 5.1 RESULTS OF REPRESENTING KNOWLEDGE FROM LARGE LANGUAGE MODELS: Generation reveals larger temporal gaps than MCQA and TF, which provide structured options that help elicit recent knowledge.The effect is especially pronounced for biomedical MCQA and legal TF settings.
- 5.1 RESULTS OF REPRESENTING KNOWLEDGE FROM LARGE LANGUAGE MODELS: Legal knowledge has the most stable static results and the largest dynamic–static gap, while generation performs lowest and TF performs highest.Its low number of object changes over time supports the observed static stability.
- 5.1 RESULTS OF REPRESENTING KNOWLEDGE FROM LARGE LANGUAGE MODELS: Domain characteristics strongly influence temporal representation: biomedical and legal knowledge is more consistent, while general-domain performance is more variable.General-domain changes in roles or affiliations are especially sensitive to temporal cues, while MCQA offers some resilience.
6 CHROKNOWPROMPT: CHRONOLOGICAL KNOWLEDGE PROMPTING
The paper reclassifies temporal answers to distinguish complete, incomplete, cutoff, and misaligned knowledge, then targets Partial Known cases with chronological prompting. The method traverses neighboring years to refine answers against the benchmark object.
- 6.1 CHRONOLOGICAL CATEGORIZATION: Known requires correct identification of all relevant objects at every year, while Unknown, Cut-off, and Partial Known represent distinct incomplete temporal patterns.Cut-off covers correctness near one year but failure elsewhere; Partial Known covers correctness for some objects or years only.
- 6.1 CHRONOLOGICAL CATEGORIZATION: Partial Known captures substantial temporal knowledge with errors at some years, motivating explicit attention to surrounding time spans.The paper contrasts this with Known, Unknown, and Cut-off categories.
- 6.2 METHOD: Figure 6 frames ChroKnowPrompt as a remedy for Partial Known cases that confuse answers across timestamps rather than lacking all temporal knowledge.The target is to move such cases toward the fully Known category.
- 6.2 METHOD: The method is designed as a non-parametric chronological elicitation strategy that integrates multiple temporal snapshots without retraining.Its broader evaluation spans unchanged objects across multiple domains and models.
- 6.2 METHOD: ChroKnowPrompt traverses preceding and subsequent spans around target time Tn, appending representative correct objects as few-shot context.The nearest timestamps are processed first, and majority voting selects representative objects from Correct and Partial Correct categories.
- 6.2 METHOD: The method initializes a target-time prompt, iteratively refines candidate answers through backward traversal, then performs forward traversal when available.If one side has no usable years, traversal proceeds on the other side.
- 6.2 METHOD: After traversal, the final candidate Cn is checked against the target benchmark object and labeled Chrono-Correct when aligned.Chrono-Correct is treated as Correct for chronological assessment.
7 EXPERIMENTAL RESULTS & ANALYSIS
ChroKnowPrompt improves recall for unchanged objects across models, especially in biomedical data, but gains are smaller in general and legal domains. Its effectiveness is limited for changed objects and varies with temporal span usage and chat-model format.
- 7.1 RESULTS OF CHROKNOWPROMPT: Biomedical unchanged-object performance rises by 11.5%, compared with 2.8% in general and 3.1% in legal domains.Known increases as Partial Known decreases across proprietary and open-source models.
- 7.2 ANALYSIS: Changed-object performance reaches only 10–30% of unchanged-object performance, averaging 0.4 in general domains.The limitation is especially challenging in complex legal contexts and motivates parametric editing research.
- 7.2 ANALYSIS: Using both previous and next spans improves scores over previous-only prompting, nearly doubling biomedical results from 6.7 to 12.0.The general-domain increase is smaller, from 1.8 to 2.8, and model sensitivity to span direction varies.
- 7.2 ANALYSIS: ChroKnowPrompt is not consistently effective for pre-instruction-tuned chat models: mpt-7b-chat improves by 11.4 on average, while Pythia-Chat-Base-7B shows almost no improvement.The comparison includes three open-source chat models.
8 RELATED WORK
Prior work studies knowledge probing, representation, editing, temporal benchmarks, and knowledge conflicts. ChroKnowledge extends this line by broadening temporal coverage across timestamps, templates, domains, and evolving versus stable knowledge.
- 8 RELATED WORK: Research on language-model knowledge spans probing, knowledge definition, representation, manipulation, and conflicts caused by evolving or contradictory facts.These studies include LAMA, representation analyses, editing methods, and knowledge-overshadowing phenomena.
- 8 RELATED WORK: Temporal-knowledge benchmarks and analyses include TimeQA, TemporalWiki, CarpeDiem, DyKnow, and fixed-age alignment approaches.These works address temporal and continuous knowledge flow, outdatedness, knowledge distributions, or alignment to one age.
- 8 RELATED WORK: ChroKnowledge broadens prior temporal evaluation by using multiple templates, wider timestamp coverage, and domains that either change or remain constant.Its temporal representation adds a time attribute to the subject–relation–object structure.
9 CONCLUSION, LIMITATION, AND FUTURE WORK
The work establishes a benchmark and framework for evaluating chronological knowledge, finding improved recall with ChroKnowPrompt but persistent difficulty with temporal evolution and unstructured contexts.
- ChroKnowBench evaluates temporal knowledge across time dependency, multiple domains, and temporal state.
- ChroKnowledge finds that models often recall timestamped facts but struggle to capture complete temporal boundaries, especially with rigid formats such as MCQA and TF.
- ChroKnowPrompt reduces Partial Known responses and increases Known responses, particularly for biomedical knowledge and unchanged objects across proprietary and open-source models.
- Dynamic datasets and unstructured, context-rich formats remain challenging, motivating parametric techniques to complement prompting for changing objects and complex temporal dependencies.
A.1.1 PARAMETRIC KNOWLEDGE UPDATE
This section presents ChroKnowBench and ChroKnowPrompt for evaluating and eliciting chronological object-level knowledge across multiple domains, data types, and temporal states. It examines domain- and template-dependent recall and shows that iterative prompting improves chronological correctness unevenly, with dynamic and unstructured legal data remaining challenging.
- A.1.2 NON-PARAMETRIC KNOWLEDGE UPDATE: Parametric updates use fine-tuning and related editable or continual-learning methods, whereas black-box models require non-parametric methods that avoid direct parameter access and catastrophic forgetting.The paper focuses on non-parametric updates across structured and unstructured formats because existing methods often struggle with unstructured data.
- A.2.4 ITERATIVE DISTRACTOR GENERATION: Evaluation uses Generation, MCQA, and TF templates, with few-shot demonstrations sampled at every timestamp and fuzzy token-set matching applied at threshold 70.Commonsense distractors receive GPT-4o verification because approximately 20% of generated options may accidentally be correct.
- A.3.1 STATISTICS OF OBJECT CHANGES IN DYNAMIC DATASET: Across dynamic datasets, average object changes are 2.6 in general data and 2.3 in biomedical data, while legal data is dominated by single changes.Biomedical changes are relatively balanced, whereas general changes are moderately skewed and legal changes are highly concentrated in one occurrence.
- A.3.2 OBJECT-LEVEL FOCUS IN BENCHMARK GENERATION: ChroKnowBench tracks object-level transformations for fixed subject-relation pairs, enabling scalable, precise, flexible, and interpretable evaluation of temporal knowledge dynamics.Its knowledge-centric design follows individual object values across timelines rather than aggregating concurrent events into temporal snapshots, supporting gradual changes in biomedical and legal data.
- A.3.4 SOURCE AND APPROACH OF BIOMEDICAL DOMAIN: The benchmark spans biomedical, legal, commonsense, and mathematics/data-structure domains, using 7k dynamic and static UMLS instances, 4,362 meaningful legal updates, CSKG objects, and 22k translated Math-KG triplets.The legal data also include 4,746 unchanged paragraphs containing detected entities.
- A.5 DETAILS OF CHROKNOWLEDGE IN LEGAL AND TIME-INVARIANT DOMAIN: Legal results are the most stable among time-variant domains, with minimal dynamic decline, while task-template differences substantially affect recall.Commonsense and mathematics show minimal temporal variation, but generation performs lower because models must recall exactly one object; MCQA and TF alleviate this tendency.
- A.6 TOTAL TIME FRAME RESULT OF CHROKNOWLEDGE: Total ChroKnowledge results report percentage-correct performance separately for dynamic and static states across general, biomedical, and legal domains and three templates.Figures 10–12 summarize total time-frame results, while Figures 13–17 provide template-wise analyses, including chat-template models such as mpt-7B.
- A.7 ALGORITHM OF CHROKNOWPROMPT: ChroKnowPrompt traverses surrounding previous and subsequent time spans, uses majority-voted candidate objects, iteratively augments prompts, and relabels a matched final answer as Chrono-correct.Traversal skips unavailable sides, accumulates prompts, and verifies the refined candidate against the original target using fuzzy matching.
- A.7 ALGORITHM OF CHROKNOWPROMPT: The algorithm obtains a correct object by majority voting, augments the accumulated prompt, generates or verifies an answer, and updates the answer set and chronological category.It returns the accumulated answers together with the updated category.
- A.8 TASK CONFIGURATIONS OF CHROKNOWPROMPT: Evaluation covers Incorrect and Partial Correct cases using 10% of each domain, fuzzy matching at temperature 0, and Chrono-correct classification only when the final candidate matches the object.The system prompt supports either answer generation or verification and refinement.
- A.9 DETAILS IN SPAN-WISE RESULTS OF CHROKNOWPROMPT: ChroKnowPrompt improves static-dataset results substantially, with the highest increase exceeding 10% in proprietary models, while dynamic gains reach only 1.9 and legal-domain gains are limited or negligible.Legal results also vary more across models, and its long-context unstructured format may contribute to low edit quality.