Source-linked AI summary
Large Language Models Can Learn Temporal Reasoning
Siheng Xiong, Ali Payani, Ramana Kompella, Faramarz Fekri
TL;DR
LLMs struggle with temporal reasoning because it requires diverse temporal concepts and intricate logic, while existing approaches do not explicitly represent its temporal structure. TG-LLM translates text into temporal graphs and reasons over them, using TGQA for training and CoT bootstrapping with graph augmentation. Experiments report better temporal-reasoning performance, transfer to other tasks and benchmarks, and reduced reasoning-error rates, while temporal commonsense reasoning still requires adaptation.
Problem
LLMs do not perform temporal reasoning sufficiently well, and existing methods do not explicitly involve the temporal-graph structure underlying complex temporal reasoning.
Method
TG-LLM translates context into a temporal graph, fine-tunes on the controllable TGQA dataset, and teaches reasoning over graphs with CoT bootstrapping and graph data augmentation.
Results
TG-LLM achieves better performance than existing pipelines, transfers TG translation to other temporal reasoning tasks and benchmarks, and its strategies reduce all assessed CoT error types.
Takeaways & Limitations
Temporal graphs and deliberate intermediate reasoning steps provide the framework's basis for producing more reliable final answers in temporal reasoning.
Takeaways & Limitations
TG-LLM still needs adaptations for temporal commonsense reasoning.
Abstract
from arXiv · showhide
While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they are not without their flaws and inaccuracies. Recent studies have introduced various methods to mitigate these limitations. Temporal reasoning (TR), in particular, presents a significant challenge for LLMs due to its reliance on diverse temporal concepts and intricate temporal logic. In this paper, we propose TG-LLM, a novel framework towards language-based TR. Instead of reasoning over the original context, we adopt a latent representation, temporal graph (TG) that enhances the learning of TR. A synthetic dataset (TGQA), which is fully controllable and requires minimal supervision, is constructed for fine-tuning LLMs on this text-to-TG translation task. We confirmed in experiments that the capability of TG translation learned on our dataset can be transferred to other TR tasks and benchmarks. On top of that, we teach LLM to perform deliberate reasoning over the TGs via Chain-of-Thought (CoT) bootstrapping and graph data augmentation. We observed that those strategies, which maintain a balance between usefulness and diversity, bring more reliable CoTs and final results than the vanilla CoT distillation.
1 Introduction
TG-LLM frames temporal reasoning as translation into a temporal graph followed by reasoning over that latent representation. The paper introduces TGQA and CoT-focused strategies to improve temporal reasoning and transfer learned translation to other tasks.
- Motivation: Temporal reasoning challenges LLMs because it combines mathematical, logical, and commonsense skills across concepts such as ordering and duration.
- Motivation: Existing approaches improve temporal reasoning but do not explicitly model its intrinsic temporal-graph structure.
- TG-LLM: TG-LLM translates context into a temporal graph and then performs reasoning over that latent representation.
- Reasoning strategies: Chain-of-Thought bootstrapping and graph data augmentation teach LLMs to generate more consistent and faithful reasoning chains than vanilla CoT distillation.
- TGQA: TGQA is a fully controllable synthetic dataset requiring minimal supervision for text–temporal graph alignment, and learned translation transfers to other temporal reasoning tasks and benchmarks.
2 Dataset Construction
TGQA is constructed from temporal knowledge-graph subgraphs to provide controllable temporal reasoning data with ground-truth timelines and diverse question types. Its samples combine temporal graphs, stories, questions, and answers, with rule-generated QA supervision and limited alignment verification.
- Dataset pipeline: TGQA uses temporal knowledge-graph subgraphs, anonymized entities, generated stories, and multiple question-answer pairs.
- Dataset properties: The dataset provides ground-truth timelines, diverse temporal reasoning question types, and applicability to various scenarios and tasks.
- Supervision: Supervision is required only for verifying story–temporal graph alignment because the question-answer pairs are generated from rules.
3 TG-LLM
TG-LLM translates text into temporal graphs before deliberate reasoning, using synthetic and verified graph data to support temporal reasoning. It combines CoT bootstrapping with graph data augmentation to improve reasoning reliability.
- Text-to-TG Translation: TG-LLM first translates text into a temporal graph, then guides the LLM to reason over that representation.The graph is generated or verified using entities, relations, and temporal information extracted from the story and questions.
- Text-to-TG Translation: The TGQA pipeline creates controllable text–temporal graph alignment data with minimal supervision and supports transfer to other temporal reasoning tasks.Stories and question-answer pairs are generated from subgraphs, with generated graphs checked against predefined questions.
- Bootstrapping Chain of Thoughts: CoT bootstrapping filters out chains with incorrect final answers and samples accepted chains using scores that balance usefulness and diversity.The score incorporates normalized probability of the correct answer and plausibility growth.
- Graph Data Augmentation: Graph data augmentation perturbs temporal graphs by removing irrelevant edges, replacing relation names with synonyms, and changing entities or times.Entity and time transformations are designed to discourage memorization of semantic information and strengthen learning of temporal logic.
- Graph Data Augmentation: The framework trains reasoning on ground-truth or verified graphs but introduces controlled disturbances to reduce the resulting train–inference discrepancy.The disturbances are designed to improve robustness without confusing the LLM.
4 Experiments
The experiments evaluate TG-LLM’s reliability, temporal reasoning performance, generalization, and module contributions across TGQA, TimeQA, and TempReason. Results show benefits from the two-step framework, CoT bootstrapping, and graph data augmentation.
- Experimental Setup: The evaluation covers TGQA, TimeQA, and TempReason using token-level F1, exact match, and perplexity-based accuracy.The authors also compare Llama2, GPT-3.5, GPT-4, SFT strategies, and T5-based models.
- Implementation Details: The Llama2-13B implementation uses separate adapters for text-to-TG translation and temporal graph reasoning, applied sequentially at inference.GPT-3.5 generates stories, temporal graphs, and CoTs, while GPT-4 creates in-context CoT demonstrations.
- Experimental Questions: The experiments evaluate CoT bootstrapping and graph data augmentation for more reliable reasoning over temporal graphs.The study compares ICL with CoTs, SFT with CoTs, bootstrapping CoTs, and graph data augmentation on TGQA.
- Generalization: SFT with TGQA and the two-step framework improves temporal reasoning across different data distributions.The authors report that SFT on TGQA outperforms ICL on TimeQA and TempReason, indicating transfer of text-to-TG translation and graph reasoning capabilities.
- Ablation Study: The ablation study finds that explicit temporal graphs, CoT bootstrapping with contrastive learning, graph data augmentation, and external knowledge each contribute to performance.The study uses Llama2-13B for all ablation configurations on TGQA.
5 Related Work
Related work improves temporal reasoning through language modeling, auxiliary knowledge objectives, prompting, fine-tuning, and external tools. TG-LLM differs by explicitly translating text into graphs before graph reasoning.
- Language-based Temporal Reasoning: Language-based temporal reasoning methods use time-aware language modeling or auxiliary objectives to incorporate temporal knowledge.The paper notes that representation learning for the underlying structure and logic of temporal reasoning is often not explicitly involved.
- Reasoning towards LMs: Reasoning-enhancement methods include in-context demonstrations, fine-tuning with intermediate reasoning steps, and combinations with domain-specific external tools.These approaches are described as reasoning-involved modeling or hybrid methods.
- Reasoning over Knowledge Graphs: Knowledge-graph research includes symbolic reasoning over temporal graphs and language-based reasoning over static graphs.The paper positions TG-LLM among approaches that use graph representations for reasoning.
- TG-LLM: TG-LLM uses text-to-graph translation as a precursor to graph reasoning, supporting broader generalization and usability than approaches limited to certain tasks.This design explicitly incorporates the underlying structure and logic of temporal reasoning.
6 Conclusion
The conclusion presents TG-LLM as a temporal reasoning framework that combines temporal graphs with intermediate reasoning steps. Experiments report better performance than existing pipelines, while future work targets inductive and abductive reasoning.
- Conclusion: TG-LLM equips language models with temporal graphs and intermediate reasoning steps to produce reliable final answers.The framework is proposed to improve language-model performance on temporal reasoning.
- Conclusion: Extensive experiments indicate that TG-LLM achieves better performance than existing pipelines.The conclusion identifies inductive and abductive reasoning as directions for future work.
Limitations
TG-LLM improves temporal-relation understanding through a graph-based paradigm and informative training data, but temporal commonsense reasoning still requires adaptation.
- Scope Boundary: TG-LLM still needs adaptations for temporal commonsense reasoning.The authors identify explicit in-context integration of commonsense as an opportunity for this task.
- Future Directions: Future improvements could verify generated temporal graphs using prior knowledge, such as plausible human lifespan ranges.The paper also suggests simulating an environment to provide feedback to the model.
Ethics Statement
The study uses publicly available datasets for research and generates text with GPT while acknowledging ethical considerations. These datasets may still contain improper or harmful content that does not reflect the authors’ opinions.
- YAGO11k, TimeQA, and TempReason are publicly available and intended for research purposes only.
- The framework uses GPT to generate text based on YAGO11k, with ethical considerations acknowledged.
- The datasets may contain improper or harmful content, which does not represent the authors’ opinions.
A Dataset Statistics of TGQA
TGQA provides controlled temporal-reasoning data with varied question categories, anonymized entities, and external temporal knowledge. Its evaluation averages performance across question categories to reduce imbalance effects.
- TGQA includes training, validation, and test samples, with about 30 question-answer pairs per sample.The dataset contains 400 training samples, 100 validation samples, and 100 test samples.
- The dataset uses global entity-name mapping and integrates mathematics and commonsense as external knowledge for temporal reasoning.
- TGQA covers eight listed question categories, including ordering, duration, time gaps, neighboring events, occurrence time, and temporal overlap.
- Question-category imbalance is addressed by computing metrics separately for each category and averaging them for final scores.
B Experiment Details
The experiments use several Llama2 and GPT model variants and include graph-augmentation details and example prompts for the framework’s data-generation and reasoning procedures.
- The implementation section includes graph data augmentation as part of the experimental details.
- The evaluated models include Llama2-13B, Llama2-70B, GPT-3.5, and GPT-4.
- Example prompts cover TGQA story generation, alignment verification, TimeQA temporal processing, graph construction, and CoT bootstrapping.
D Fine-grained Results of TGQA
Fine-grained TGQA results compare models and strategies using exact match, while noting parsing and context-length constraints. The proposed strategies progressively improve performance across question categories.
- TGQA fine-grained evaluation reports exact match for models using different strategies, including zero-shot settings where applicable.
- GPT-3.5 is used to parse some generated answers because the rule-based parser cannot reliably handle the generation format.This parsing may introduce evaluation errors.
- CoT bootstrapping, graph data augmentation, and external knowledge progressively improve performance, with the resulting model achieving the best overall performance across categories.