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TRAM: Benchmarking Temporal Reasoning for Large Language Models

Yuqing Wang, Yun Zhao

arXiv:2310.00835v3cs.CL

TL;DR

Prior temporal reasoning studies have focused on time-sensitive question answering and explicit temporal elements, while a unified framework spanning broader aspects such as narratives and causality was lacking. TRAM combines ten temporal reasoning tasks, with 38 subtasks and 526.7k multiple-choice questions spanning foundational concepts through advanced interpretations and computations. GPT-4 achieves an average accuracy of up to 84.4% but falls short of human proficiency by over 10%, while models struggle with nuanced understanding.

  • Problem

    Prior temporal reasoning studies have focused on time-sensitive question answering and explicit temporal elements, while a unified framework spanning broader aspects such as narratives and causality was lacking.

    Source: §1 · p. 1

  • Method

    TRAM combines ten temporal reasoning tasks, with 38 subtasks and 526.7k multiple-choice questions spanning foundational concepts through advanced interpretations and computations.

    Source: §1 · p. 1

  • Results

    GPT-4 achieves an average accuracy of up to 84.4% but falls short of human proficiency by over 10%, while models struggle with nuanced understanding.

    Source: §1 · p. 2

  • Takeaways & Limitations

    TRAM provides a unified evaluation of rudimentary temporal concepts and complex narratives, highlighting implicit-cue and temporal-relationship challenges for future model improvement.

    Source: §5 · p. 8

    Subset test-set evaluation may not reflect the full spectrum of LLM temporal reasoning; multiple-choice questions may permit guessing, and text-only questions omit multimodal cues.

    Source: §6 · p. 9

Abstract

from arXiv · show

Reasoning about time is essential for understanding the nuances of events described in natural language. Previous research on this topic has been limited in scope, characterized by a lack of standardized benchmarks that would allow for consistent evaluations across different studies. In this paper, we introduce TRAM, a temporal reasoning benchmark composed of ten datasets, encompassing various temporal aspects of events such as order, arithmetic, frequency, and duration, designed to facilitate a comprehensive evaluation of the TeR capabilities of large language models (LLMs). We evaluate popular LLMs like GPT-4 and Llama2 in zero-shot and few-shot scenarios, and establish baselines with BERT-based and domain-specific models. Our findings indicate that the best-performing model lags significantly behind human performance. It is our aspiration that TRAM will spur further progress in enhancing the TeR capabilities of LLMs.

1 Introduction

Temporal reasoning remains difficult for language models, while prior work has lacked a unified framework spanning its broad facets. TRAM brings together ten temporal reasoning tasks, and evaluations show that even the strongest models remain below human performance.

1 Introduction

  • Prior temporal reasoning work has focused mainly on time-sensitive question answering and explicit temporal elements, leaving narratives, causality, and a unified broad-facet framework unaddressed.The paper notes that models had not reached human-level performance in this domain.Source: §1 · p. 1
  • TRAM combines ten tasks, 38 subtasks, and 526.7k questions spanning foundational temporal understanding through advanced interpretations and computations.Answers combine expert annotation and programmatic generation; questions use multiple-choice rather than generative formats.Source: §1 · pp. 1–2
  • The study evaluates BERT, RoBERTa, domain-specific RST, and LLMs; LLMs use standard and chain-of-thought prompting in zero-shot and few-shot settings, with GPT-4 reaching up to 84.4% average accuracy.Source: §1 · p. 2
  • GPT-4 achieves average accuracy of up to 84.4% but falls short of human proficiency by over 10%, while models struggle with nuanced understanding and implicit cues.The reported error pattern spans all task categories.Source: §1 · p. 2

2 Related Work

Prior temporal reasoning resources have addressed distinct tasks, while language-model research has shifted from fine-tuning toward zero-shot and few-shot prompting. The paper positions TRAM as a broad evaluation framework and benchmarks traditional, domain-specific, and recent LLM systems.

Temporal Reasoning Benchmarks

  • Existing temporal benchmarks range from temporal-relation datasets to multi-task evaluations and time-sensitive QA, but often specialize in narrower facets such as duration, frequency, or event-time relations.The paper presents TRAM as seeking a more complete representation of temporal reasoning challenges.Source: §2 · p. 2

Training Paradigms in LLMs

  • Language-model evaluation has moved from fine-tuning BERT-style models toward zero-shot and few-shot adaptation, alongside prompting methods including CoT, self-consistency, tree-of-thought, and metacognitive prompting.The passage describes pretraining as standard practice and GPT-3 as shifting focus toward minimal fine-tuning.Source: §2 · p. 2
  • The study establishes baselines with BERT-based models, a domain-specific temporal reasoning model, and recent LLMs to compare strengths and limitations across diverse tasks.The described models include Llama2 and GPT-4.Source: §2 · p. 3

3 Tasks and Datasets

TRAM organizes temporal reasoning into ten multiple-choice tasks spanning foundational understanding, temporal interpretation and computation, and advanced conceptual reasoning. Its datasets combine diverse sources and construction strategies across these task families.

3 Tasks and Datasets

  • Ten tasks are grouped into foundational understanding, interpretation and computation, and advanced temporal and conceptual understanding, with one correct answer per multiple-choice question.The groups cover ordering, frequency, duration, typical time; ambiguity resolution and arithmetic; and relation, temporal NLI, causality, and storytelling.Source: §3 · p. 3
  • The benchmark contains 526,668 problems, with five development questions per category and a separate test set; most tasks use accuracy, while relation and temporal NLI also use F1.For relation and temporal NLI, the passage attributes the additional metric to imbalanced fixed label distributions.Source: §3 · p. 3

3.1 Foundational Temporal Understanding Tasks

  • Foundational tasks assess ordering, frequency, duration, and typical time using commonsense, reading-comprehension, factual, comparison, and computation problems drawn from datasets and constructed examples.Ordering includes commonsense and historical-event questions; frequency and duration include tailored templates and questions selected from MCTACO or SQuAD.Source: §3.1 · pp. 3–4

3.2 Temporal Interpretation and Computation Tasks

  • Interpretation and computation tasks test ambiguity resolution across temporal expressions and calendar shifts, alongside arithmetic such as date, time, month, week, year, and time-zone calculations.Questions and answer-forming programs derive from human expertise.Source: §3.2 · p. 5

3.3 Advanced Temporal and Conceptual Understanding Tasks

  • Advanced tasks assess temporal relations, temporal inference, causality, and story endings using adapted existing datasets, keyword-filtered examples, and manually crafted problems.Relation questions use TempEval-3 Silver; temporal NLI draws on SNLI and MNLI, causality adapts COPA-style questions, and storytelling uses ROCStories and the Story Cloze Test.Source: §3.3 · pp. 5–6

4 Experiments

The experiments compare language models and human performance across TRAM, reporting both benchmark results and recurring error patterns.

4 Experiments

  • The report selects the best results across multiple runs for each experimental setting.Source: §4 · p. 6

4.1 Experimental Setup

  • LLMs were tested with standard and chain-of-thought prompting in zero-shot and 5-shot settings, using up to 300 test examples per category.Greedy decoding was used; 5-shot examples came from the development set, and CoT answers were human-annotated.Source: §4.1 · p. 6
  • BERT-style and domain-specific RST baselines used limited fine-tuning data, while expert annotators answered about 1,900 questions across TRAM.Training samples were selected at rates depending on original dataset size, and models shared the LLM test sets.Source: §4.1 · pp. 6–7

4.2 Overall Performance Comparison

  • Over 9%: GPT-4 leads second-best Gemini under 5-shot CoT, yet remains 12.9% behind human performance.GPT-4 outperforms other models on the majority of tasks.Source: §4.2 · pp. 7–8, Table 2 · p. 7
  • LLM arithmetic performance ranges from 52.6% to 91.2%, while RoBERTa-large exceeds Llama2 in average performance.BERT and RoBERTa also perform exceptionally on temporal relation, contrasting with their average or below-average performance elsewhere.Source: §4.2 · p. 7, Table 2 · p. 7

4.3 Error Analysis

  • Assumption bias leads foundational-task errors (32%), calculation slips dominate interpretation and computation (42%), and implicit oversights lead advanced-task errors (34%).Source: §4.3 · p. 8, Figure 2 · p. 8

5 Discussion

The discussion presents TRAM as a broad tool for probing temporal reasoning, highlighting contrasts in model performance and future benchmark extensions.

5 Discussion

  • TRAM spans ten diverse temporal reasoning tasks, and contrasting model performances highlight the significance of experimental strategies and intrinsic challenges.Source: §5 · p. 8
  • The benchmark supports unified evaluation from rudimentary temporal concepts to complex narratives and highlights implicit cues and intricate temporal relationships as challenges.It is presented as a tool for identifying model limitations and guiding further advancements.Source: §5 · p. 8
  • Planned extensions include more test data, tailored prompting, varied question formats, and fine-tuned open-source models such as Llama3, Phi-3, and Gemma.Source: §5 · p. 8

6 Limitations

The authors identify limits in test-set coverage, multiple-choice evaluation, and text-only questions.

6 Limitations

  • Subset test evaluation may not reflect the full spectrum of LLM temporal reasoning, and multiple-choice questions may permit random guessing.Source: §6 · p. 9
  • The authors identify these concerns as limitations of TRAM's assessment.Source: §6 · p. 9
  • Textual questions may omit the multimodal cues, including images and videos, present in real-world scenarios.Source: §6 · p. 9

A Datasets

The datasets section describes TRAM's documentation and construction materials, while noting that the listed templates are not exhaustive.

A Datasets

  • The section provides a datasheet, subtask-definition glossary, dataset-construction details, and example questions.Source: A Datasets · p. 10
  • Construction details include human-crafted templates for programmatic question generation and temporal-keyword filtering of existing datasets.Source: A Datasets · p. 10
  • For multi-subtask tasks, the section reports distributions, but the displayed templates do not cover the full set used in construction.Source: A Datasets · p. 10

A.1 Datasheet for TRAM

The datasheet presents TRAM as a broad temporal-reasoning benchmark, documenting its intended scope, composition, quality checks, and task definitions. It also specifies the benchmark’s distribution and maintenance arrangements.

OVERVIEW

  • TRAM targets foundational temporal understanding, temporal interpretation and computation, and advanced temporal and conceptual understanding.Its stated examples include duration and frequency, ambiguity resolution and arithmetic, and causality and storytelling.Source: OVERVIEW · p. 10

DETAILS

  • 526,668 problems span 10 main tasks and 38 subtasks in a benchmark curated to cover a broad range of temporal-reasoning scenarios.The benchmark includes multiple-choice questions drawn from existing datasets and human-curated problems.Source: DETAILS · p. 11, OVERVIEW · p. 11
  • 526,668 questions use multiple-choice answers, and problems drawn from existing datasets are reformulated as standalone items with distracting options and irrelevant questions filtered out.The benchmark is partially self-contained; remaining questions are heavily human-curated, with programmatic generation also used.Source: DETAILS · p. 11
  • Problems may have ambiguous interpretations, similar variants, or source-related option issues, although professionals review problems and verify a unique correct answer.The authors report that further verification checks will be conducted to minimize errors or noise.Source: DETAILS · pp. 11–12

ADDITIONAL DETAILS ON DISTRIBUTION AND MAINTENANCE

  • The benchmark is publicly distributed through its official GitHub page, with older versions retained when updates are made.The first release was in September 2023; the first author supports and maintains the benchmark.Source: ADDITIONAL DETAILS ON DISTRIBUTION AND… · p. 12

A.2 Task Glossary Definitions

  • The glossary defines task operations such as arranging events chronologically, determining event frequency, measuring duration, and identifying typical event timing.These definitions clarify the temporal skills represented by the named tasks.Source: §A.2 · p. 13

A.3 Data Construction

TRAM combines timeline-derived, template-generated, curated, and filtered data to construct temporal-reasoning tasks. Its appendix provides examples for several tasks, including ordering and frequency.

A.3 Data Construction

  • Ordering facts are derived from historical events in Wikipedia timelines spanning ancient history through the 21st century.The authors explored available timeline pages for individual centuries.Source: §A.3 · p. 14
  • Ambiguity resolution uses templates and gathered temporal expressions, while arithmetic uses programmatically generated values in templates for varied calculations.Arithmetic templates range from time adjustments to week identification and real-world applications.Source: §A.3 · p. 14
  • Relation questions are built from TempEval-3 Silver links and their event or time contexts, while temporal NLI selects SNLI and MNLI instances using temporal keywords.The temporal NLI filtering is intended to capture a broader range of temporal relationships and nuances.Source: §A.3 · pp. 14–15
  • Frequency and duration include template-based subtasks, with Table 3 identifying templates for frequency comparison, computation, and applications.The table caption states that placeholders represent variable event and time inputs.Source: §A.3 · p. 14, Table 3 · p. 15
  • Causality problems incorporate varied temporal factors, while storytelling instances from ROCStories and SCT are selected using keyword-based filtering.The storytelling keyword selection accounts for varied and colloquial temporal expressions in ROCStories.Source: §A.3 · p. 15

A.4 Example Questions

  • Figures 3 and 4 show example questions for ordering and frequency, respectively; the appendix says correct choices are bolded.The appendix also directs readers to figures with examples for duration, typical time, ambiguity resolution, arithmetic, and advanced temporal tasks.Source: §A.4 · p. 15, Figure 3 · p. 16, Figure 4 · p. 16

A.5 Comparison of Source vs. Curated Datasets

TRAM’s curated questions transform source-dataset items into multiple-choice formats, while its prompts use direct questioning or structured step-by-step reasoning. The appendix also reports subtask distributions, including an even split between causality problem types.

A.5 Comparison of Source vs. Curated Datasets

  • MCTACO yes/no questions were reformulated as multiple-choice frequency and ordering tasks, while SQuAD short-answer questions were adapted for duration.Curation also added distractors, filtered irrelevant questions, and reformulated problems.Source: §A.5 · p. 16

A.6 Subtask Distributions

  • 50% of causality problems belong to each of its two problem types.The appendix defines tasks with more than one problem type as involving multiple subtasks.Source: §A.6 · p. 16

B Prompts

  • SP presents questions directly, whereas CoT guides models through context, key-element extraction, evaluation, comparison, and a reasoned conclusion.The CoT procedure varies by task.Source: B Prompts · pp. 16, 19

C Human Assessment

The human assessment draws on screened experts and non-specialists answering the same questions. MTurk non-specialists achieved 63.5% accuracy across 1,900 questions, providing a broader human-performance baseline.

Selection of Expert Annotators

  • Expert annotators included professionals with advanced degrees in cognitive science, psychology, statistics, mathematics, and computer science.The selection aimed to balance temporal-reasoning and quantitative expertise.Source: C Human Assessment · p. 19

Expertise Verification Process

  • Experts had to exceed 92% average accuracy on a 37-problem screening assessment, allowing at most three incorrect answers.The assessment included one random problem from each subtask.Source: C Human Assessment · p. 20

Comparison with Unspecialized Individuals

  • 63.5% overall accuracy across 1,900 questions was achieved by MTurk non-specialists, providing a baseline for general human performance on TeR.They answered the same question set as the experts.Source: C Human Assessment · p. 20

D Error Types

The error analysis groups model failures into foundational temporal understanding, temporal interpretation and computation, and advanced temporal and conceptual understanding, identifying distinct pitfalls in each group.

Foundational Temporal Understanding Tasks

  • Foundational tasks expose assumption bias, descriptor misinterpretation, event ambiguity, and missed or misread contextual clues.Source: D Error Types · p. 21

Temporal Interpretation and Computation Tasks

  • Computational and interpretable tasks reveal calculation slips, qualitative-to-quantitative descriptor confusion, vague-time resolution errors, and time-notation mix-ups.Source: D Error Types · p. 21

Advanced Temporal and Conceptual Understanding Tasks

  • Advanced tasks include missed implicit cues, simplified event relations, familiar-story bias, and overgeneralized temporal conventions.Source: D Error Types · p. 21
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