Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning
Authors: Bahare Fatemi
Mehran Kazemi
Anton Tsitsulin
Karishma Malkan
Jinyeong Yim
John Palowitch
Sungyong Seo
Jonathan Halcrow
Bryan Perozzi
Venue: ICLR 2025 Poster (2025)
Keywords: Temporal Reasoning
Temporal Graphs
LLMs
Primary area: datasets and benchmarks

Abstract: Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex temporal logic. Existing research has explored LLM performance on temporal reasoning using diverse datasets and benchmarks. However, these studies often rely on real-world data that LLMs may have encountered during pre-training or employ anonymization techniques that can inadvertently introduce factual inconsistencies. In this work, we address these limitations by introducing novel synthetic datasets specifically designed to assess LLM temporal reasoning abilities in various scenarios. The diversity of question types across these datasets enables systematic investigation into the impact of the problem structure, size, question type, fact order, and other factors on LLM performance. Our findings provide valuable insights into the strengths and weaknesses of current LLMs in temporal reasoning tasks. To foster further research in this area, we will open-source the datasets and evaluation framework used in our experiments.

Published as a conference paper at ICLR 2025
TEST OF TIME: A BENCHMARK FOR EVALUATING LLMS ON TEMPORAL REASONING
Bahare Fatemi', Mehran Kazemi p Anton Tsitsulin', Karishma Malkan Jinyeong Yim?, John Palowitch", Sungyong Jonathan Halcrow', and Bryan Perozzi Gooole Research, Goooge DeepMind, GGoogle
ABSTRACT
Large language models (LLMs) have showcased remarkable reasoning capabil- ities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex temporal logic. Existing research has explored I.I.M performance temporal reasoning using diverse datasets and benchmarks. How- ever, these studies often rely on real-world data that LLMs may have encoun- tered during pre-training employ anonymization techniques that can inadver- tently introduce factual inconsistencies. In this work, we address these limi- tations by introducing novel synthetic datasets specifically designed LLM temporal reasoning abilities in various scenarios. The diversity of question types across these datasets enables systematic investigation into the impact of the problem structure. size, question type. fact order, and other factors on LLM performance. Our findings provide valuable insights into the strengths and weak- nesses of current LLMs in temporal reasoning tasks. The dataset is available at: itttps://huginggaace co/ co//aattaeetts/baaee////
assess
INTRODUCTION
Recent breakthroughs in large language model (LLM) research and applications have been signif-
icant (Vaswani et al. 2017 Devlin et al., 2019 Raffel et al., 2020 Brown al., 2020 Touvron et al., 2023: Achiam et al. 2023 Team et al. 2023 Reid et al. (2024). These models, capable of generating new content, have fascinated the Al community, leading to the release of numerous
LLMs trained on diverse tasks and data types (Zhao et al. 2023 All of these advancements have led to growing consensus that LLMS are a pivotal advancement on the path to artificial general intelligence (AGI) (Bubeck al., 2023). Benchmarking reasoning capabilities in LLMs is therefore a problem of pressing interest to the field (Huang & Chang 2023).
In this work, we focus temporal reasoning, essential task for intelligent systems across many domains. Temporal reasoning is focused on understanding reasoning between events in time. Despite this area's 's importance, existing temporal reasoning benchmarks do not effectively measure the full scope of temporal reasoning relationships. Instead, they typically rely on question-answert tasks based on Knowledge Graph (KG)-style temporal facts about well-known entities.
This overemphasis on KG-style temporal facts limits the scope of research and creates several issues. First, it neglects the diverse temporal structure and reasoning tasks found in real-world applications beyond KGs. Second, the results on such data often reflect model's 's ability to exploit prior know wedge rather than genuine temporal reasoning, making findings less relevant to domains where models lack this knowledge (see Figure D as an example.). In addition, previous research has shown that shortcuts or heuristics can often answer questions on these datasets without explicit temporal reasoning (Chen) et al. 2022 Tan et al.) 2023). Finally, the simple temporal structure of knowledge graphs overlooks the extensive time arithmetic skills required in real-world temporal questions.
Our Contributions: To address these limitations, we develop tasks specifically designed to assess temporal reasoning in a more comprehensive and controlled manner. Our benchmark, Test of Time, ToT, is centered around the observation that temporal reasoning often involves two primary skills: 1) understanding the semantics and logic of time, and 2) the ability to carry out temporal arithmetic.
"Equal contribution. Correspondence: (baharef, mehrankazemi :gooog ee com.

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Published as a conference paper at ICLR 2025
Prompt: Below are the list of head coaches for Chelsea FC.
Prompt: Below are the of head coaches for club. Who the coach before ES?
Who was the coach before Pochettino?
Pochettino: July 2023 to May 2024
ES: July 2023 to May 2024
Potter: Septemher 2022 to April 2023
E4: Septemher
April
Tuchel: January 2021 to September 2022
E3: January 2021 to September 2022
Lampard: July 2019 January 2021 and April 2023 to June 2023
E2: July 2019 to January 2021 and April 2023 to June 2023
Sarri: July 2018 June 2019
E1: July 2018 to June 2019
Model Response: The coach before Pochettino was Frunk Lampard during second stint with the club from April 2023 to June 2023.
Model Response: the coach before ES.
Grounded prompt (answered correctly)
Abstract prompt (answered incorrectly)
Figure 1: Comparison of the same temporal query using real (left) and anonymized (right) entity names. Gemini Advanced correctly answered the query with real names but failed with anonymized names, suggesting that LLMs might rely on their parametric knowledge to solve temporal tasks.
ToT has two tasks to cover each essential skill of temporal reasoning, which enable measuring and improving model performances along these two axes independently. task, focuses on temporal semantics and logic; it allows for flexible exploration of diverse graph structures and reasoning task complexity, isolating and evaluating reasoning abilities independent of prior knowledge. FoT-Arithmes calculations involving time points and durations. Our experimental results with ToT provide valuable insights into the strengths and weaknesses of current LLMMs in temporal reasoning tasks.
synthetic
a crowd-sourced task, assesses the ability to perform
RELATED WORK
Reasoning, The ability to draw valid conclusions from explicitly provided knowledge has been fundamental goal for AI since its early days (McCarthy) 1959] [ewitt] 1969). In the past few years, several M.M--aeeed techniques have been developed which have advanced the general automated reasoning capabilities of the state-of-the-art models (Wei et al. [022 Yao et al. (023), or their capa- bilities in specific directions including mathematical reasoning (wekkowyz et al. 2022 Ahn et al.) 2024), logical reasoning (Creswell et al. 2022 Kazemi et al. 2023b), multi-modal reasoning (Wang al. 2024), commonsense reasoning (Zellers al.) 2019), and more. Advancing reasoning may
explicitly or implicitly translate to improvements in several downstream NLP applications.
Temporal reasoning. Temporal reasoning has recently gained substantial attention (e.g.. Vashishtha
ct al. 2020 Nylund et al.] 2023 Hu et al.) 20233 Gurnee Tegmark, 2023 Liu et al. [023 Xiong
al. 2024 Beniwal al. 2024 Jia et al. (0024). Much research focuses enhancing L.LMS
understanding of temporal concepts, primarily through pre-training and fine-tuning strategies to
improve their temporal reasoning capabilities (e.g., Ning al. 2019 Zhou al. 2020 Yang et al..
2023
Xiong et al. 2024 Jia et al. (2024).
Benchmark creation another active area, with many benchmarks centered on knowledge graphs (e.g..
Jia et al. 2018 /eelam et al. 2021] Jia et al. 202T Wang & Zhao 2023 Chu et al. 2023 Su et al.) 2024). While TempTabQQA (uupta et al. 2023) offers crowd-sourced questions based on Wikipedia infoboxes, the process is resource- intousic-intensive and prone to issues like LLM overuse by workers. The questions in Wang & Zhao (023) are all multiple-choice, and do not require reasoning through a many temporal facts from a knowledge graph. The questions in Chu et al. (202)) are collected from ten existing real-world datasets, one of which requires reasoning through temporal facts provided in the context. In contrast, ToT goes beyond such datasets by providing controllable, comprehensive temporal relationship collections via synthetic graph generation. The questions in Timo Su et al. (2024) are grouped into categories: math-time and pure-time. Tot-Artithmetic covers more domains in the math-time category and more focus on reasoning in the pure-time category. Xiong et al. (2024) recently proposed TGQA, data set derived from the YAGO1 1 knowledge graph (Dasguptal
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Table 1: Comparison of ToT against related benchmarks.
Real World
Semantics
Arithmetic
Synthetic
Hermetic
Implicit
Benchmark
TimeSensitiveQA (Chen et al.
2021
X
X
X
StreamingQA (Liska et al.
2022
X
X
TempLama (Dhingra et al.
2022
X
X
TEMPTABQA (Gupta al.
2023
X
TEMPREASON Tan et al.
2023)
TIQ (Jia et al. 2024)
X
x
TempUN BBenwwal et al., 2024
x
TGQA (Xiong et al.
2024)
x
ToT (ours)
et al. 2018). To prevent data leakage, TGQA changes each entity name to name generated by GPT3.5 that is guaranteed to (i) align with the entity's ' type and (ii) not be otherwise present in YAGO11K. 1k. This strategy has several weaknesses. First, it introduce spurious entity name correlations (LLMs could even potentially guess the original entities due to their adjacent relations). Second, it can generate factually incorrect or anti-commonsnniial claims, for instance, an entity's generated replacement name is real name that is nonetheless not in YAGO1 Finally, relying on GPT for copying facts introduces the potential for hallucinations contaminate the dataset.
Synthetic datasets. A new trend in probing various LLMs capabilities, especially in the case of reasoming, is through synthetic data which allows for a more systematic evaluation. Previous work has developed synthetic datasets for probing reasoning capabilities including logical reasoning (Tafjord
al. 2021 Kazemi al. 2023c Saparovv al. 2023) and mathematical reasoning (Kazemi et al.
2023a Srivastava et al.] 2024). Most similar to our work, |Fatemi et al. (2024) develop a synthetic
probe for measuring the graph- reasoning abilities of LLMs (Sanford et al. 2024 Perozzi et al. Our work extends this concept to the case of temporal reasoning with graph-like facts.
2024
Present work. In this work, we introduce ToT, novel benehmark for temporal reasoning generated synthetically. Unlike many existing benchmarks that rely on knowledge graphs, ToT aims to encompass wider variety of graph structures. Our synthetic generation approach offers precise control over the type of data produced. Importantly, when evaluating LLMS against ToT, they cannot exploit their latent knowledge for shortcuts; instead, they must genuinely reason with the presented facts. This design promotes more rigorous assessment of temporal reasoning capabilities in LLMs. Table D provides comprehensive comparison of ToT with existing benchmarks across six key dimensions: 1- Semanties: whether the benchmark has semantic-type questions, Arithmetic: whether the benchmark has arithmetic-tyen questions, 3- Real-world: whether the benchmark has questions generated from real-world data, 4- Synthetic: whether the benchmark has questions generated from synthetic data, 5- Hermetic: whether the benchmark is sealed off from potential I.LM training data, and 6- Implicit: whether the benchmark includes implicit questions. Our analysis reveals that ToT is unique in incorporating all these question types while effectively mitigating training data leakage. Notably, TEMPREASON (Tan et al.) 2023) only covers one category of the arithmetic operations defined in Section 3.2
3 TOT: A BENCHMARK FOR EVALUATING LLMS ON TEMPORAL REASONING
We propose that effective temporal reasoning hinges on two distinct skills: understanding the semantics and logic of time, and performing accurate temporal arithmetic. To measure and improve model performance along these independent axes, we create a dedicated task for each skill. By decoupling the evaluation of temporal semantics from arithmetic, aim to provide a more nuanced analysis of LLM capabilities, pinpointing strengths and weaknesses each aspect. Experiments on these tasks enable to independently benchmark L.LM performance both dimensions.
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1. Generate graph
2. Assign entity and relation names
3. Generate temporal facts
EII was the R21 I223 from 1983 to 1985.
E23
E23 the R21 E32 from 2007 2013.
RO1
ES1 was the R17 of E23 from 2004 to 2009.
R17
was of E4 from
E32
FS1
R30
Generate a question
Which entity was the R17 of E23 at the time
D
E4
when E32 started being
E23?
Figure Steps for creating the ToT- SerT--Sematiii dataset.
3.1 TOT SET-SENANIC:: A SYNTHETIC DATASET
The first task we introduce, ToT-Semantia consists of synthetic problems designed to highlight temporal semantics and logic reasoning. This task is unique because it allows us to freely experiment with wide range of temporal dependencies and manipulate the difficulty of the reasoning problem. This allows us to isolate and amalyze the core reasoning capabilities of an LLM, separating them from any reliance pre-existing parametric knowledge. To create the ToI- SorT-Semataal ic task, we take the following steps (summarized in Figure 2
Step 1: Generate (random) graph. We begin by generating random structures that we will then use to create temporal questions. To ensure we generate a diverse set of random structures for this purpose, turn to the literature graph structure generation. From it, employ several existing algorithms for generating graphs of varying properties. This includes Erdös-Rényi (ER) graphs (Erdös & Rényi 1959), scale-free networks (SFN) (Barabási & Albert 1999), graphs following the Barabási-Alber (BA) model (Albert & Barabási 2002) and stochastic block model (SBM) (Hol
land et al. 1983). as well as star and complete graphs. Each of these graph generation algorithms exhibits different properties and correspond to graphs that appear in different applications. For instance, Erdös-Rényí graphs are typically sparse with low average degree, while Barabási-Albes graphs are dense and exhibit power-law degree distributions. We leverage the NetworkX library for generating our random graphs Additionally, we extracted anonymized EgoNets from Wiki- Data (Vrandečić & Krötzsch 2014) by replacing the entity and relation names with generic names. We refer to this structure as Anonymized Wikidata Extract (AWE) in our experiments. We generate graphs with the number of nodes selected uniformly random from the [5-30] interval. More details on the random graph generators used (with visualizations) are available in Appendix
Step 2: Assigning entity and relation names. Once we have initial graph structure, assign names to the nodes and relations to the edges. For each graph, we first decide a number of relation types to be assigned to the edges, and assign each of these relation types one of one-to-one, one-to-many, many-to-one and many-to-many. Then, for each edge in the graph, we randomly assign between p (=3 in our experiments) relations types without violating the relation type arity.
Step 3: Generate temporal facts. Then, for each edge (u, v) labeled with a relation r. we assign valid time interval [t1, #2] that respects the relation types, and turn the tuple (u, v, r, t2) into textual temporal fact using template.
Step 4: Question generation. Having generated the random graphs, we then create questions about those graphs. We consider eight types of questions that are frequently used in day-to-day life and are common in various benchmarks. EventArTimeT: asking which entity had some relation R with entity E at some T; EventA El and F2 started/ended; MumEEvenssnTimmeeneee
asking at what time a relation R between two entities asking how many entities had relation with entity E between TI to T2: BeforeAfter: asking which entity had relation R with El right before/after E2; SventAttimeOOAAoottee entity had relation R2 with E3; FirstLast: asking which entity was the first have relation R with E; RelationDuration: Asking the k-th time relation R happened between E1 and E2, how long did last; and timeline: Asking sort the entities that had relation R with E chronologically.
asking when El had relation RI with E2, which
To create any of the above questions, we keep sampling graphs and fact(s) from the graph until a proper question of the desired type can be created for that graph and for that fact. For example, to
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Seed Set We selected a seed quactions
Expand
Filter We ditered know! olge hesvy and cu-- cise qucslions
Calegorize
The aumotatoos expended seed set inoo lagg questions.
We grouped the quactiiess based the required ariimmeti operations.
Punctionalize
implemened guments are sumpled tinal unswers calculated using python litrzies.
quosion, impur
Sumple
days funct
generated dataset sampling question and answers from the codes.
def cdd Says Istact staee__tins, snd_tine))
dace = pandon_data 1)
in randon, randint (10,100)
quest on = ease todey ia Idstel, what in the day Inis days. from now? onewer sarzent_day dotctine satecinn.einedclee wimedciit (days suara suusttion,
Figure 3: Steps for creating the ToT Sarithmetin ic dataset. The green and blue colors represent the operations done by the authors and the annotators respectively.
T2) e G create a BeforeAfter question, we keep sampling a graph and fact F (S, T,,,,,,,, until we have case where there is unique entity E that was the R of O right before T1,T22
Following the above two steps, we generated 10 questions per graph generation and per question type. We sorted the facts in five different ways as will be discussed later. This gives as a benchmark with total of 7 8 * 10 2800 questions, where is the number of graph generation algorithms, is the number of question types, 5 is the number of sorting algorithms, and 10 is the number of samples we generated. Example questions of each category type are shown in Table
3.2 TOT-ARITHMETLE TEMPORAL ARITHMETIC DATASET
Our second task, ToT-Ari thmet c, shifts from synthetic data to a real-world focus. This task moves beyond understanding the logic and semanties of time and delves into the practical application of mathematical operations within temporal context. Through it, we are able to measure an LLM's proficiency in temporal arithmetic and its practical utility in handling time-related computations.
To create a large time-arithmetic dataset that covers a wide variety of problems, we took the steps summarized in Figure We explain each step in more detail below.
Seed Set: By examining the existing benchmarks and the kind of temporal arithmetic questions that arise them and through searching the web, we gathered a small set of initial questions.
Expand: We presented our seed set to 15 annotators who were tasked to propose either new time arithmetic questions that were scenarios or question templates where one requires to do similar time arithmetic operations to one of the questions in our seed set. We gathered large list of questions through this process.
our seed set, or to provide questions corresponding other
Table 2: Example for each question type in the ToT-Semant i c dataset.
Question Type
Example
EventAfTimeT
Find the entity that evidently was the R17 of E69 in year 1932.
EventAtWhalTime
At what time did E69 start being the R90 of E22?
NunEVentslaTiimenneess
Find the number of unique entities were R82 EE7 between 1952 to 1957. Relations that ended in 1952 or started in 1957 must be counted.
BeforeAfter
Immediately before E59, which entity was the R20 E6?
EvenAAYTimeOAAAnohhrEEvenn
E94 was the R82 of which entity at the time when E83 started being the R20
of E59?
FirstLast
Which entity was the first that was the R35 of E91?
RelationDDration
When E24 the R53 of E1 for the 2nd time, for how many years did the relation last? The duration can be computed by subtracting the start time from the end time.
Timeline
Which entities were the R17 E69?
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Filter: We manually went through all the questions and filtered the ones that were focusing corner cases, or that required extensive knowledge (e.g., requiring to memorize the entire calendar).
Categorize: We then grouped the remaining problems into seven categories, shown with examples in Table Categories are formed based on the time arithmetic operations required, as follows: AddSubtract: adding or subtracting number (corresponding to days, weeks, minutes, hours, etc.) from date or time: Compare: comparing a number of dates/times provided in different formats chronologically; Duration: computing the difference between two dates/times; Schedule: finding mutual free spots within multiple blocked times; Timezone: involving dealing with different timezones; Trick: involving questions with slight twists; and MultiOp: involving questions where multiple of the above operations are needed.
Following (Srivastava et al. 2024), we implemented a functional version each problem enable sampling different values for each question and solving based on those values. A functional version of one of our simple problems is provided in Figure 3
Funcionalizing:
Sampling: We then sampled questions and answers from our functionalized problems, proportional to the number of different problems that fell under each category. Specifically, we sampled 350 for AddSubtract, 350 for Compare, 200 for Duration, 250 for Schedule, 100 for Timezone, 250 for Trick, and 350 for MultiOp. This resulted in a dataset with 1850 questions in total.
3.3 QUALITY CHECK
For both tasks, we did multiple rounds of quality checks where we verified 1) whether the generated labels correct, and 2) whether the question is clear and the provided instructions sufficient to know in what format the output should be produced. This procedure was done until no more issues could be found in the dataset.
Table 3: Examples for each question type in the
ic dataset.
Category
Example
AddSubtract
Your driver's license expires on 18 May, 2017. You receive renewal notice saying it can be renewed 117 days in advance. What's the earliest date you can renew your license?
Compare
E42 was discovered in April, BC and E11 was discovered in 05 October, 530 BC. Which was discovered carlier?
Duration
Stella and William were born on 1999-Dee-16 and 2000-Oct-03 respectively. When William was 400 days old, how old was Stella in days?
Schedule
Lucas is available from 11 to noon and also from 3:30 to 5. Asher available from 11 to 12:30 and also from 4 to 5. They want to have 30 minute mecting. The mecting has to start the hour or half hour. How many possibilities are there for the meeting time?
Timezone
Flight departs location A at 1 11:08 (24hr) UTC(+0000), It reaches location B at 07:23:20 PM IST(+0530). What is the total time duration taken to fly?
Trick
If the date for the day before tomorrow in yyyy-mm-dd format is 2016-01-20, what is the date 27 days from in the same format?
MultiOp
Alex solves 2 puzzles in 4 hours, 50 minutes, and 22 seconds. What is the time taken by them to solve puzzles, at the same pace.
EXPERIMENTS AND RESULTS
In this study, we evaluate the performance of five frontier large language models (LLMs) on our bench- mark. The models evaluated include Claude-3-Somin (Anthropic 2024h. Mistral Large (2407) (Team) 2024). GPT-4 (Achiam et al.) 2023). Gemini 1.5 Pro (Reid et al. 2024). and GPT-4o (OpenAl (2024). For GPT-44 employed GP 1- Turbo for the T i task, it supports larger context size, and standard GPT for the ToT-Ari hmet ic task due to its superior performance. The same variant of GPT-4o was used for both tasks]
The experiments were done Summer 2024 with the latest versions of the models the time.

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Table 4: LLM accuracy on temporal reasoning tasks by graph structure.
Gruph
Claude-3-Someet
Mistral Large
GPT-4
Gemini 1.5 Pro GPT-4o
Average
BA
48.50
63.00
63.25
62.75
72.00
61.90
Complete
34,00
32.75
40.25
52.50
51.00
42.10
ER
42.25
42.25
48.75
60.50
62.25
51.20
SBM
42.00
48.50
50.75
57.75
61.75
52.15
SFN
58.75
77.75
75.25
75.75
86.00
74.70
Star
59.50
77.50
80.25
74.25
81.75
74.65
AWE
68.75
88.50
92.00
87.50
94.00
86,15
Average Rank
4.75
3.50
2.75
1.43
1.12
In experiments, aim answer the following questions:
RQ1: What is the effect of the temporal structure on the LLM performance?
RQ2: What kind of temporal questions easier/harder for LLMs to answer?
RQ3: How important is the order of the facts and what is the best way of ordering them?
RQ4: How well do the frontier models perform time semantics and time arithmetic?
4.1 INVESTIGATING THE IMPACT OF TEMPORAL STRUCTURE ON LLM TEMPORAL REASONING
In different applications where temporal reasoning arises, the structure of the facts can be different. Some tasks may provide all the information about an entity (corresponding to star graph) and ask questions about it, whereas in some others, such as social networks, the structure of the facts may follow a power-law distribution. We study whether the inherent temporal structure of problem might influence LLM'' ability graph neural networks (Palowitch et al., 2022: Tsitsulin et al. 2022 Yasir et al., 2023 Fatemi et al. 2024). this section aims to quantify how different temporal dependencies affect an LLM's temporal reasoning capabilities using graph generators to create many different kinds of temporal structure.
its data. Drawing inspiration from recent work analyzing
The graph structure of the temporal relationships significantly affects LLM performance, as demon- strated in Table Notably, GPT-4 accuracy more than doubled between complete graphs (40.25%) and AWE graphs (92.00%). Also, Mistral Large accuracy varied drastically across graph types, from 32. 32.75% for complete graphs to 88.50% for AWE graphs. This highlights critical gap in temporal reasoning research, which has largely overlooked the diverse graph structures and reasoning tasks found in real-world applications, instead focusing primarily on specific knowledge graphs (like YAGOI Ik). This may explain the superior performance of LLMs on AWE graphs our experiments, with GPT-4o nearly solving the task with 94.00% accuracy.
4.1.1 INFLUENCE OF GRAPH SIZE ON LLM PERFORMANCE
Table Average number of nodes and edges by graph structure.
A key question is how different models behave function of the size of a graph, measured terms the number of edges (facts) and nodes (entities). As illustrated in Figure increasing either the number of edges or nodes in the ToT-Semant dataset mostly leads decrease in LLM performance. We observe, however, that different models are affected differently. For example, for the smaller graphs with < 250 edges, GPT-4o outperforms the other models, whereas when the size increases to > 1000 edges, Gemini 1.5 Pro outperforms the other models. Moreover, we observe that while the performance of GPT-4o and Gemini 1.5 Pro does not degrade much after a certain point of increasing the number of edges (specifically, for the last three buckets), other models' performances keep decreasing (with the exception of GPT-4 at the last bucket).
Graph
#nodes
#edges
BA Complete
17.41
144.07
17.25
619.86
ER
16.18
316.4
SBM
17.51
368.15
SFN
17.52
53.46
Star
16.16
34.12
AWE
18.99
25.41
Average
17.29
223.07
The above results raise the question of whether the graph structure's impact observed in Section 4.J merely a consequence of varying graph sizes. To address this, we present the average number of nodes and edges for each graph structure in Table While the average number of nodes does not appear to consistently influence LLM performance across structures, the number of edges does show some correlation. However, there exceptions. For instance, SBM graphs have far more edges
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I
GPT4o E Gemini 1.5 Pro Pro4 M Mistral Large 8 Selaude-3-Sonaae
100
80
80
Accuracy,
60
60
40
40
20
20
< 250
250-500
500-750
750-1000
>> 1000
10
10-20
> 20
Number of edges
Number of nodes
Figure 4: Accuracy of models for different number of edges and nodes.
overage than ER graphs, yet the average performance of models across ER graphs is lower than SBM graphs. Also, SFN graphs have far more edges on average than Star graphs. yet GPT-4o performs better on SFN graphs than Star graphs. This indicates that both the number of edges and the specific structure of the graph play a significant role in determining LLM performance. As for number of nodes, AWE graphs have more nodes average compared the other graph structures, yet the average performance of models across AWE is the highest across all (see Table 4
4.2 EFFECTS OF TEMPORAL QUESTION TYPE ON LLM TEMPORAL REASONING
Table 6: LLM accuracy on temporal reasoning by question category.
Temporal Question Type
Claude-33 3-oude---Sonee Mistral Large
GPT-4
Gemini 1.5 Pro GPT-4o
Average
EventArTime'T
47.14
64.86
65.43
72.29
71.43
64.23
EventArWhatTime
90.29
90.00
89.43
93.14
96.86
91.94
CumeEvenssnnTimmeneers
29.71
57.14
61.43
59.14
63.71
54.23
BeforeAfter
53.14
56.57
55.43
52.86
64.00
56.40
EvenAATTimccOAAnnheeeeeee
50.00
57.43
67.14
71.43
75.71
64.34
Firstl .ast RelationDuration
68.57
57.71
67.71
68.57
73.71
67.25
41.43
76.57
80.00
84.57
88.86
74.29
Timeline
24.00
31.14
28.29
36.29
38.57
31.66
Average Rank
4.31
3.75
3.37
2.44
1.12
In this experiment, we systematically investigate the impact of different temporal tasks the reasoning ability of LLMs. We quantify this impact by evaluating model performance across a variety of tasks, summarized in Table
Task type and reasoning requirements. A key question in our investigation is whether the type of temporal task and the associated reasoning requirements influence LLM performance. The ToT-Semant ic dataset includes questions of varying difficulty levels, which can be categorized based on the reasoning type: Single-fact solutions: Questions EventArTimeT and EventAtWharTimm require retrieving one single fact and answering the question directly based on the retrieved fact. Multi-fact solutions: The remaining questions require retrieving multiple facts and performing operations (e.g., counting, sorting) to extract relevant information and formulate answer.
LLMS consistently demonstrate superior performance on tasks requiring the retrieval of a single fact compared those necessitating the integration of multiple facts. This performance gap can be attributed to the increased cognitive demands associated with multi-fact tasks. While single- fact questions primarily rely the identification and extraction of relevant information, multi-fact questions demand a deeper comprehension and synthesis of retrieved information.
Performance variations within question types. Even within zero-order reasoning tasks, LLMs demonstrate varying levels of proficiency. For example, EventAtTimeT and EventAtWhatTime structurally similar, yet L.LMs tend to excel at the latter. We hypothesize that this performance
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difference may be attributed to the fact that EventArTimeT requires simple time arithmetic operation to recognize that a timestamp T falls within a time interval [T1, T2], whereas EventAtWhat does not require any time arithmetic operation.
Analysis on Timeline questions. Timeline questions are the most difficult category of ques- tions for the models according to Table [ An analysis of these questions reveals that they pose the greatest challenge across all tasks. To answer these questions, typically structured as "Sort the entities that were the R17 of E69 chronolog- ically?", the model needs to extract multiple entities (in the ToT-Semant- i c dataset, every timeline question has more than one entity in its label), and then do temporal arithmetic to sort them temporally. To further analyze the models these questions, we calculated the average precision and recall for each model in Table where precision shows what percentage of the entities extracted by the model are correct entities (i.e. must be included in the timeline) and recall shows what percentage of the correct entities have been extracted by the model. We report the results once averaged over all graph structures and once only for complete graphs (the most challenging graph structure). Gemini 1.5 Pro demonstrates superior precision and recall, aligning with its relatively high accuracy observed in Table 6 The only model outperforming Gemini 1.5 Pro on timeline questions is GPT-4o. The fact that the precision and recall of GPT-4o is lower than that of Gemini but its overall performance on timeline questions is higher shows that Gemini is better retrieving the correct entities but worse at arithmetic operation (as also confirmed later in Section 4.4) Moreover, GPT-4, despite having higher accuracy than Souue---Sonnee on timeline questions, exhibits the lowest precision and recall. This suggests that GPT-4 frequently outputs fewer entities than are present in the true answers (50% of the times), leading to missed correct entities (lower recall) and a higher proportion of false positives among its predictions (lower precision).
Table 7: Precision and recall on timeline questions.
All
Complete
Graph structure
Precision
Recall
Precision Recall
Slanne- Mistral Large GPT-4
0.73
0.75
0.56
0.54
0.62
0.65
0.30
0.33
0.60
0,56
0.36
0.23
Gemini 1.5 Pro GPT-4o
0.81
0.83
0.82
0.65
0.78
0.74
0.69
0.51
Since complete graphs pose the greatest difficulty among all graph structures (Table we provide a separate analysis of average precision and recall for these graphs in the final two columns of Table 7 Notably, all models except Gemini 1.5 Pro experienced declines in both precision and recall on complete graphs, whereas Gemini was primarily impacted in terms of recall.
Table 8: LLM accuracy on temporal reasoning tasks function of the order of the facts.
Order of facts
3-nnnet-Sonnet Mistral Large GPT-4
Gemini 1.5 Pro GPT-4o Average
Shuffle
45.71
55.71
60.71
63,04
68.93
58.82
TiletinnAndSturTTimee
54.29
63.93
65.36
68.57
72.14
64.86
HaanfTimeAAdRRellnn
47.68
59.11
60.54
64.64
65.89
59.57
StartTimm And Target
49.11
60.89
61.61
65.18
70.00
61.36
TargetAndStarTTinnn
73.57
67.50
62.60
75.00
81.07
71.95
4.3 IMPACT OF TEMPORAL FACT ORDER ON LLM PERFORMANCE
noteworthy question arises regarding the potential influence of fact order on LLM performance in temporal reasoning tasks. To investigate this, we conducted experiments on ToT Semantic dataset. We sorted the facts using different methods: Shuffle: randomizing the order of facts; Rela- fonAAnStaarTTime: prioritizing facts based on their relation name, then by start time: StartTime- AndRelation: prioritizing facts based on start time, then by relation name; StartTime Target: prioritizing facts based on start time, then by the target entity: TargetAndStartTime: Prioritizing facts based on the target entity, then by start time.
Ideally, L.MS should exhibit robustness to the order in which facts are presented, given the inde- pendent nature of each fact. However, as shown in Table g our observations reveal a significant impact of fact order on LLM performance. Notably, performance is consistently lowest when facts are presented in shuffled order and consistently highest when facts are sorted based on the target entity and start time TTagetAndStarrTime are only slightly better than the shuffled order, thus revealing that not any kind of ordering is ideal for LLMs. This finding offers valuable practical insights into how facts should be structured when temporal reasoning is key component of the LLM task. By organizing
We also observe that some sorting strategies such as

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facts in manner that aligns with the temporal flow of the narrative task, can potentially enhance LLM performance and ensure more accurate and reliable results. While previous work has shown that ordering premises in the correct order of chain-of- chain-of-thought solution improves LLM's logical reasoning (Chen et al. 2024; Saparov & He 2022), our results extend that to general-purpose temporal orderings (independent of the chain-of-thought)
Table LLM accuracy on the PoT-Arithmetic c dataset by question type.
Category
Claude-3-Sonnet Mistral Large GPT-4
Gemini 1.5 Pro GPT-4o
Average
AddSubtract
58.57
76.28
71.14
76.29
68.68
Compare
39.14 14
62.29
63.14
55.43
66.57
$7.30
Duration
15.00
17.50
16.00
13.50
15.00
15.40
Schedule
29.60
44.40
43.60
40.00
53.20
42.16
Timezone
74.00
87.00
88.00
90.00
92.00
86.20
Trick
40.40
44.80
45.60
41.20
53.20
45.04
MultiOp
26.57
54.86
46.86
62.57
46.86
47.54
Average Rank
4.71
2.71
2.43
3.28
1,57
4.4 TEMPORAL SEMANTICS VS TEMPORAT. ARITHMETIC
This study examined the performance of temporal arithmetic capabilities in LLMs using the dataset. Results, as shown in Table 9 indicate that the models consistently excelled in Timezone questions, while struggling the most with Duration questions. This superior performance Timezone questions could attributed to the abundance of information about various timezones available online, compared to other question types. Scheduling and Trick questions also proved challenging for LLMs, likely due to their creative nature and requirement for deeper reasoning. In contrast, AddSubtract results were relatively strong, potentially reflecting LLMs' optimization for mathematical reasoning and their ability to apply that knowledge to temporal reasoning tasks.
Analysis on Duration questions. Analysis of Duration questions in the ToT Arit hmet i c dataset revealed them to be the most challenging for the evaluated models. Notably, the most common deviation of precisely one day from the ground truth label.
error among incorrect answers Specifically, when GPT-4 or Gemini 1.5 Pro erred on Duration questions, approximately 21 and 25% of its responses within one day of the ground truth, respectively. This suggests that LLMs can approximate the correct calculation but often stumble in the final steps, highlighting gap in their ability execute complex arithmetic with precision.
Common failure: direction. One frequent error in ToT-Ari Arithmetii occurs when determining the number of months between dates. For example, from February 11th, 2002, to October 1tth, 2002, the correct duration is eight months, but the model sometimes incorrectly calculates it as four months. This issue is particularly noticeable questions that involve going back in time, such as: "Sam's birthdate is October 11th, 1996. Today is February 25th, 2002. Calculate Sam's 's age in days."
Common failure: leap year calculation. Another frequent error in ToT-Ari perrtthttiiio arises when determining the number of days between two dates that span multiple years. Incorrectly accounting for leap years, which have an extra day (February 29th), often leads to inaccurate results.
5 CONCLUSION
In conclusion, we introduce Test of Time (ToT), a novel benchmark designed to assess LLMSs temporal reasoning abilities in more comprehensive and controlled manner than existing work. Our two-pronged approach, encompassing both semantic and arithmetic tasks, enables a nuanced evaluation of temporal reasoning. Through extensive experiments with ToT, have gained valuable insights into the strengths and weaknesses of current LLMs in these critical aspects of temporal reasoning. By poun-sou datasets and evaluation framework, hope stimulate further research and development in this field, ultimately contributing to the advancement of LLM capabilities in complex reasoning tasks.
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A DESCRIPTION OF GRAPH GENERATORS.
Here we detail each graph generator used to create the examples in ToT. We note that every collection of temporal facts, where each fact is relationship between two entities, can be expressed as a temporal graph with nodes as entities. ToT specifically targets LLM reasoning ability over such collections. We do not claim that graph generators are the only way to construct such benchmark. However, because all temporal fact collections contain an underlying graph, we propose generation framework based on graph models to produce benchmark examples. We argue that framework that exposes generation of the static graph backbone is more controllable and allows for benchmark that is more comprehensive w.r.t. the variety and complexity of temporal relationships between generated entities.
First, we cover the six random graph generators used to create the synthetic examples. All random graph generators are probabalistic models which take hyperparameters that control the expected macro-prev pretrorropettees of each graph (Palowitch et al. 2022
Erdōs-Rényi (ER) (Erdós & Rényi 1959), This model takes edge probability parameter p and generates each edge with probability p. i.i.d. over all possible edges.
Scale-Free Networks (SFN) (Barabási Albert 1999 graph is grown by sequence of steps, each step either (1) adding new node and connecting it to an existing node. or (2) adding an edge between two existing nodes. Input parameters control the probability of these events. This process generates a "scalc-free" power law of node degrees, in sharp contrast to the ER model.
Barabási-Alberr (BA) model (Albert & Barabási 2002): a graph is grown by a sequence steps, each step adding a new node to the graph, and connecting the node to m existing nodes with probability proportional to their current degree. Similar to SFN, this process also generates a "scale-free" graph with a particular distribution known as the Barabási-Albe model.
Stochastic Block Model (SBM) (Holland et al. 1983 y This graph model can be thought of as clustered ER. It divides nodes into 1 clusters, and then connects two nodes with probability p if they are in the same cluster, else with probability if they are in different clusters. k. p, and q are all hyperparameters.
A star-graph generator creates "star" graph on n nodes: node 0 is the center of the star, and all other nodes connect (and only it).
A complete-graphe generate creates "complete" graph on n nodes, in which all nodes are connect to each other node.
An example from each of the above graph generators shown in Figure 5 In the figure, edges are annotated with temporal relationships in the format relat ion id: [interval] interval []] Note that each edge can have multiple relationships, and each relationship can have multiple intervals. The visualization shows the diversity of temporal knowledge graphs that our framework is able to generate. We note that while our study was limited to parametric graph generators in this work, the field of graph machine learning (Chami et 2022) offers many options for both modeling (Perozzi et al. 2014) and learning (Halcrow et al.
2020 Fatemi et al.] 2021
Rozemberezki et
2021
Fatemi al. 2023) link structure.
Second, we describe our Anonymized Wikidata Extract (AWE) strategy for creating anonymized questions from real-world data. We first identify the 78 most common relations in WikiData that specify time-bound entity relationships. Each relation encodes temporal edge between two entities. To match the schema of our synthetic graphs, we convert each time specification on each edge to an interval. Then, for each entity in the graph, we extract the ego-graph of the entity by (1) collecting the entity and all its neighbors and (2) collecting all edges (along with temporal information) between nodes collected in (1). This process produces temporal graph with a schema identical to those produced from random graph generators. Before generating questions from the graphs, we anonymize them by (a) mapping each entity name to unique identifier such E679; and then (b) mapping each relation name to A unique identifier such as R3, We then generate questions from the graph as described in 3.1
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BA
SFN
ER
25
101 LIS. 201
BERO 201
D1 TEE
201
(16. 13T
122 2011
2011
S8M
COMPLETE
STAR
112N,
FOR FED F4t DF
U2A 231 EL 100)
2: 11b 301)
COS SON
ASD
2: 3:02
1811
11, 2311
2011
Figure 5: A visualization of representative graph from each graph generator: Frdōss-Rén's (ER), Scale-Free Networks (SFN), Barabási-Allert (BA), Stochastic Block Model (SBM), star-graph, and complete-graph.
B DETAILS OF QUESTION GENERATION.
Given graph with temporal facts, generating logically-consisten questions from our list of diverse question types (see Table 2 is non-trivial. To generate the total question set, we loop through generated graphs, choose question type uniformly-at-randee type template with facts from the graph. The exact algorithmic procedure is given below. Note that the SAMPLEFACTS routine will vary significantly depending on the question type. For some questions, it is sufficient to generate a single fact and check if the question can be generated. For other question, multiple facts must sampled (sometimes sequentially, a BFS fashion) and checked for cohesion with the template. We do all of this in a brute-force manner.
and then attempt fill the question
Algorithm 1 Generate all questions from a certain question type template.
1: Procedure GENTEAATFQUESSTIONSSC template, m)
2: <<<a
3: for i E [r]] do
4: + SAMPLEGRAPI((C)
5:
q GENERATTEQUESTIONNC: template, templall,
6: if q = ó then
7:
continue
8:
end if
9:
Q Qu)
10:
end for
return Q
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Published as a conference paper at ICLR 2025
Algorithm 2 Generate a single question from graph with maximum trials
1: Procedure GENERATEQQESSIONNC; template, m)
2: q - Φ
3: for j E [m]] do
4:
+ SAMPLEFACTS(G, template)
5: q template(F)
6: q ( á then
7: break
8: end if
9: end for
10: return
C LARGE-SCALE TOT-SEMANTIO EXPERIMENTS
To facilitate more comprehensive analysis and enable deeper insights, we expanded our synthetic dataset significantly. This enlarged dataset now encompasses approximately 50, 000 examples, substantial increase from the previous of around 3, 000 examples. We anticipate that this expanded resource will prove valuable for future research endeavors that necessitate larger and more diverse synthetic dataset. Due the computational demands of evaluating all LLMs on this large dataset, results are reported solely for Gemini 1.5 Pro.
Impact of Graph Structure on LLM Accuracy. Our initial experiment with this expanded dataset involved replicating the graph structure analysis. As illustrated in Table [O graph structure continues to exert significant influence on the final accuracy of the LLM, even within this larger dataset.
Table 10: LLM temporal reasoning by graph structure on the larger set of PoT-Semantic.
Graph Structure Accuracy (%)
70.96
Complete
51.07
ER
61.85
SBM
60.32
SEN
79.13
Star
73.77
AWE
88.72
Average
69.40
Impact of graph structure and temporal task on LLM performance. Our second experiment examined accuracy of the model across various question types and graph generators. The expanded dataset provided sufficient examples per category, enabling more robust results. The results are reported in Table IT] Consistent with our earlier findings, single-fact questions generally outperformed multi-fact questions. Notably, the highest accuracy observed for EventA(What (imeeee in single-fact questions and RelationDuratii in multi-fact questions. This alignment with the results from the smaller dataset reinforces their significance and suggests that the smaller dataset serves reliable proxy for the larger one.
Impact of Graph Structure and order of facts on LLL Performance. In this experiment, we evaluated LLM performance across various combinations of graph structure and fact order. The results, presented Table Table [2] reveal that the target_and_start start_timee ordering consistently yields the best performance across the expanded dataset, regardless of graph structure. Conversely, the shuffle ordering consistently underperforms across most graph structures.
D EVALUATION PROCESS
We adopted a structured approach to ensure consistent evaluation. The LLM prompts incorporate specific guidelines for output formatting, requiring JSON structure with fields like 'explanation'
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Table 11: Impact of graph structure and question type on a larger ToT Semantic.
Temporal task
BA
Complete
ER
SBM
SFN
Star
AWE
Average Rank
EventArTimeT
74.46
54.22
65.54
68.07
80.84
76.75
91.93
3.57
EventAtWhatTime
98.19
81.69
90.72
90.48
98.31
98.43
97.95
BeforeAfter
53.49
34,46
48.07
45.66
68.55
58.80
73.98
7.00
Eremttttimeeffnnnoheetteenn
76.99
52.89
62.53
65.18
84.82
85.78
90.48
3.79
FirstLast
70.84
49.04
61.69
55.66
87.23
68.80
92.53
4.43
57.71
40.84
54.22
49.64
64.22
70.84
83.73
6.14
RurationDuration
88.55
80.60
83.49
82.77
87.47
88.80
90.48
2.36
Timeline
47.47
14.82
28.55
25.06
61.57
41.93
88.67
7.71
and 'answer'. This standardized output facilitated automated evaluation through parsing the JSON, extracting the answer field(s), and comparing the golden label. Here examples of instructions in the prompt (please see below for the full prompt):
Example from ToT-Semantic: Answer the following question based on the temporal facts assum- ing the facts are unidirectional. Output only valid JSON string with two fields: "explanation" and "answer". Do not output anything else. The explanation field contains your reasoning. The answer field contains the value corresponding to your final answer.
Example from ToT-Arithmetic: Return your answer as a JSON in the following format: JSON "explanation": <your step by step solution>, "answer": "day_or of week".
This prompting method ensured clear instructions for the model's output format. Our experiments showed consistent adherence to these instructions, demonstrating the effectiveness of our prompt design and leading to robust and straightforward evaluation process.
Table 12: Impact of graph structure and sorting type on a larger set ToT-Semantic.
Order of facts
BA
Complete
ER
SBM
SFN
Star
AWE
Rank Average
relation_ande and_aanddaa sand_start_dtee simet_timee
73.42
52.03
64.98
61.45
81.93
74.32
90.36
2,00
shuffle
66.72
44.65
54.74
54.14
74.17
72.74
85,02
4.71
start_time_and caime_and__eheee relation
67.55
46.31
57.76
55.72
77.86
72.14
88.48
4,00
start time. and target
68.60
46.61
58.96
55.95
78.31
70.78
88.63
3.29
target_anne socct__nd__tate start_ time
78.54
65.74
72.82
74.32
83.36
78.84
91.11
1.00
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Example from ToT- RoT-Semantic
Prompt: Here a set of temporal facts:
E92 was the R17 of E69 from 1980 to 1988.
E69 was the R17 E24 from 1972 to 1981.
E59 was the R17 of E69 from 1999 to 2000,
E69 was the R90 E42 from 1997 to 2001.
E11 I was the R17 of E69 from 1977 to 1980.
E92 was the R17 of E69 from 1995 to 1999.
E69 was the R90 of E24 from 1977 to 1978.
E69 was the R90 E42 from 1980 to 1982.
E69 was the R17 of E42 from 1986 to 1994.
E92 was the R90 of E69 from 1993 to 1996.
E69 was the R90 E42 from 1974 to 1975.
E59 was the R90 of E69 from 1999 to 2000.
E69 was the R90 E42 from 1983 to 1987.
Answer the following question based on the temporal facts assuming the facts are unidirectional. Output only valid JSON string with two fields: "explanation" and "answer". Do not output anything clse. The explanation field contains your reasoning. The answer field contains the value corresponding to your final answer.
Question: Find the entity that was the R90 of E69 immediately after E92 the R90 of E69.
Model response (Gemini 1.5 pro): ("explanation": "E92 was the R90 of E69 from 1993 1996. The entity that was the R90 of E69 immediately after E92 was E59 from 1999 to 2000.", "answer": "E59")
Label: E59
Example from RoT-Arithmetic
Prompt: If right now, it is 22:00 (24hr) in Location A (EST -0500), what time is it in Location B (PST -0800)? Answer with time and day in json. Eg: ISON = "exxlanation":: <your step by step solution>, "day": bame_day/+xx>>x day=same_dayy if the answer is the same day, or +x/-x, where is the number of days before/after the start day, and time is in 24-hour format. Assume standard time without daylight saving for all timezones. If seconds aren't 't specified, assume it to be 00.
"time": "HH:MM:SS", where
("explanation": "EST is 3 hours ahead of PST. To convert 22:00 EST to PST, subtract hours from 22:00. This results in 19:00 PST.". "day": day,, "time": "19:00:00")
Model response (GPT-40):
Label: ("day": "same "same_day", "time": "19:00:00")
E QUALITY OF INSTRUCTIONS
critical question is whether the instructions the prompts of format of the output are of high quality and easily understood by large language models (LLMs). Notably, Gemini 1.5 Pro, GPT-4, and Claude-3-Sonnet missed the instructions only 0.05%, 0.65%, and 4.76% of the time, respectively. This low error rate suggests that the instructions of high quality. Consequently, we were able to establish a benchmark that allows us to focus purely on the temporal reasoning abilities of the models.
regarding the
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F INSTRUCTIONS TO PARTICIPANTS
For the crowd-sourcing section in creating the ToT-Arithmet.l dataset (Expand step), we gave the following instructions the annotators.
Time Arithmetic Benchmark Compilation
Thank you for participating in our eval hour to help us expand our dataset to cover all the categories of time arithmetic that we can think of.
Terminology:
Calculations with time values, often involving years, months, days, hours, minutes, seconds.
Time arithmetic:
Category: A high-level category of time arithmetic operations, such as addition/- subtraction, time conversion, etc.
Examples: Real-life sentences that fall into a category. For instance, "Today is 27 July 2020 and was told that my furniture will be delivered to me exactly 60 days from now. On what date will the furniture be delivered?" is an example of addition.
Goal: Our goal is to cover as many real-life categories subcategories related to time arithmetic as possible. We also want each subcategory to have multiple different real-life examples.
Levels of Importance of Contributions:
1. Discovering/adding
category.
2. Adding new real-life examples within subcategory (please contribute more in less densely populated areas).
Corner cases are useful, but please don't 't focus all your time on them. Discovering broader categories would be the most useful!
Please try to add new examples which are different from existing ones possible. Thanks!
G REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we provide the following resources and information:
Benchmark creation: A detailed description of the construction methodology for temporal reasoning benchmark is available in Section This includes the process of creation of both ToT Semantic and SuT-Arithmetha
LLM access: The LLMs evaluated in this study are publicly accessible via API calls. We specify the names of the LLMs and the corresponding versions used for our experiments Section
Evaluation procedure: Appendix D outlines the evaluation procedure used for our study along with some examples to better clarify the procedure.
To foster further research in this area, we are open-sourcing the datasets and evaluation framework used in our experiments:
H LIMITATION AND FUTURE WORK
The current work has several limitations that provide avenues for future research:
Single-Sentence Time Anchoring This benchmark focuses on scenarios where the start and end times of fact are both mentioned within single sentence. However, in real-world scenarios,
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temporal information can be spread across multiple sentences documents. It is worth noting that this setup easily convertible to the more general case where temporal information can be spread across multiple sentences. While we chose to focus the single-sentence setup for this initial work, future research could readily adapt the benchmark to the multi-sentence scenario and explore the challenges and opportunities it presents.
Exclusive Focus on Explicit Temporal Facts (By Design). This benchmark intentionally focuses solely on explicit temporal facts (those with clear time anchors), excluding static facts (those without time anchors). This deliberate choice was made to ensure the benchmark specifically targets and assesses models' capabilities in temporal reasoning. However, future work could expand the scope to include static facts, offering a more comprehensive evaluation of both temporal and general factual reasoning.
22


Reviews:
[{"review_id": "z1vQAqvv4k", "reviewer": "Reviewer_oZRw", "summary": "This paper introduces two datasets specifically crafted to evaluate large language models (LLMs) on temporal reasoning across diverse scenarios. The authors argue that existing benchmarks for temporal reasoning primarily use question-answering tasks based on Knowledge Graph -style temporal facts about well-known entities. Such benchmarks may reflect a model’s capacity to leverage prior knowledge rather than assess true temporal reasoning skills. To this end. the proposed datasets aim to measure two core temporal reasoning abilities of LLMs: (1) understanding the semantics and logic of time, and (2) performing temporal arithmetic.", "questions": "As mentioned in weakness.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- For the ToT-Semantic dataset, designed to evaluate LLMs on temporal semantics and logic, the authors employ seven graph generation algorithms and develop eight manually crafted question types. This diversity allows the generation of a large volume of synthetic questions, adding rigor to the dataset and covering various temporal reasoning facets.\n\n- The study provides detailed insights into the temporal reasoning capabilities of frontier LLMs, including how factors such as graph size, question type, and temporal fact ordering influence performance. These observations offer valuable understanding into both the strengths and limitations of current LLMs in temporal reasoning.", "weaknesses": "- While ToT-Semantic focuses on temporal semantics and logical reasoning, the paper does not clearly explain how the graph generation process ensures the correctness of graph evolution. Specifically, the distinction between generating static graphs and those with temporal dynamics is not addressed, leaving questions about the dataset's fidelity to real-world temporal processes. \n\n- In introduction, the paper emphasizes the importance of evaluating LLMs on temporal reasoning but does not clearly explain why a graph structure is essential for this assessment. Could the authors elaborate on the necessity of graphs in this context?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8CZKQpcarl", "reviewer": "Reviewer_5y55", "summary": "This paper focuses on evaluating the temporal reasoning abilities of large language models (LLMs). The authors introduce a new synthetic dataset, Test of Time (ToT), which consists of two tasks: ToT-Semantic for temporal semantics and logic, and ToT-Arithmetic for temporal calculations. The study evaluates five LLMs and analyzes the impact of factors like graph structure, question type, and fact order on performance. The findings provide insights into LLMs' strengths and weaknesses in temporal reasoning.", "questions": "1.\tHow would the performance of LLMs change if the benchmark included static facts in addition to explicit temporal facts?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "-\tThe proposed ToT benchmark is designed to address the limitations of existing benchmarks by encompassing a wider variety of graph structures and question types, enabling a more nuanced evaluation of LLMs' temporal reasoning abilities\n-\tThe authors offer an evaluation of temporal reasoning by decoupling it into semantic and arithmetic aspects. This two-pronged approach provides a more detailed analysis of LLM capabilities.", "weaknesses": "-\tAs mentioned in the limitation section, the benchmark focuses on scenarios where both the start and end times of a fact are mentioned within a single sentence. But real-world temporal information can be spread across multiple sentences or documents.\n-\tThe authors generate questions using templates, which might not fully capture the complexity and variability of natural language found in real-world temporal reasoning tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LvDxAXf2dQ", "reviewer": "Reviewer_CVKy", "summary": "Dealing with the dataset quality and potential leakage problems, this paper introduces a novel method to synthesize a benchmark for comprehensive temporal reasoning benchmarks. The benchmark contains semantic and arithmetic questions with fine-grained topology control. Extensive experiments are conducted and show insightful conclusions.", "questions": "- Some details are missing.\n    - Line 212: “we generated questions per graph generation and per question type”: Please explain how to generate such questions. Are they generated from templates, manual annotations, or LLMs?\n    - Line 369: Is it because the superior performance on longer contexts? Is there a correlation between long-context performance (or overall task performance e.g., MMLU, GSM8K, MATH500) and the final temporal reasoning performance? Are there sufficient test cases with more edges for providing robust evaluation?\n- Typos:\n    - Line 275: Funcionalizing → Functionalizing", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The data synthesis process benefits from the graph-guided control, and could be generalized to many other tasks.\n- The constructed data are comprehensive and include many perspectives with quality control.\n- Experiments are extensively conducted on multiple aspects, and provide some insights on future directions.", "weaknesses": "- Some claims lack of quantitative evidence:\n    - “real-world data that LLMs may have encountered during pre-training or employ anonymization techniques that can inadvertently introduce factual inconsistencies” Could you add some quantitative evidence showing the GPT-4 or Gemini-1.5 Pro baselines have pre-training data contaminations?\n    - “LLMs could even potentially guess the original entities due to their adjacent relations” This also lacks of quantitative evidence. If this is a commonsense, there should be relevant references cited.\n- The literature review is not sufficient, and there are many researches on math-related temporal reasoning tasks. There lacks of relevant references in the introduction and the related work.\n    - Wang, Y., & Zhao, Y. (2023). Tram: Benchmarking temporal reasoning for large language models. *arXiv preprint arXiv:2310.00835*.\n    - Chu, Z., Chen, J., Chen, Q., Yu, W., Wang, H., Liu, M., & Qin, B. (2023). Timebench: A comprehensive evaluation of temporal reasoning abilities in large language models. *arXiv preprint arXiv:2311.17667*.\n    - Su, Z., Zhang, J., Zhu, T., Qu, X., Li, J., Zhang, M., & Cheng, Y. (2024). Timo: Towards Better Temporal Reasoning for Language Models. *arXiv preprint arXiv:2406.14192*.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qWuETseGyE", "reviewer": "Reviewer_SQWS", "summary": "In this work, the authors introduce two novel synthetic datasets, TOT-Semantic and TOT-Arithmetic, specifically designed to evaluate LLMs’ temporal reasoning abilities with graph-like facts from two perspectives: (1) understanding the semantics and logic of time, and (2) performing accurate temporal arithmetic. The authors also conduct extensive experiments to examine how LLM performance is influenced by the graph structure, graph size, question type, and fact ordering of the problem.", "questions": "1. Have the authors considered how the format of the date/time, such as words versus numerical format, might influence the model’s performance?\n\n2. For 4.1, 4.1.1, what task does the author evaluate?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The method works on temporal reasoning with LLM, an important area of research that contributes to understanding the model's overall complex reasoning capabilities.\n\nThe authors conduct several experiments. Their analysis and the data offer valuable insights for future research.", "weaknesses": "The paper lacks detail on dataset construction. For instance, how are the final questions generated in both TOT datasets? Are templates being used? (see also question 1)\n\nThe number of baselines is limited. Additional approaches could include directly generating code for TOT-Arithmetic or applying few-shot or self-consistency.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)