TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models (2311.17667v2)

Abstract: Grasping the concept of time is a fundamental facet of human cognition, indispensable for truly comprehending the intricacies of the world. Previous studies typically focus on specific aspects of time, lacking a comprehensive temporal reasoning benchmark. To address this, we propose TimeBench, a comprehensive hierarchical temporal reasoning benchmark that covers a broad spectrum of temporal reasoning phenomena. TimeBench provides a thorough evaluation for investigating the temporal reasoning capabilities of LLMs. We conduct extensive experiments on GPT-4, LLaMA2, and other popular LLMs under various settings. Our experimental results indicate a significant performance gap between the state-of-the-art LLMs and humans, highlighting that there is still a considerable distance to cover in temporal reasoning. Besides, LLMs exhibit capability discrepancies across different reasoning categories. Furthermore, we thoroughly analyze the impact of multiple aspects on temporal reasoning and emphasize the associated challenges. We aspire for TimeBench to serve as a comprehensive benchmark, fostering research in temporal reasoning. Resources are available at: https://github.com/zchuz/TimeBench

Evaluating Temporal Reasoning in LLMs: An Analysis of TimeBench

The paper "TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in LLMs" systematically addresses a crucial gap in the evaluation of LLMs by introducing a benchmark specifically designed to assess temporal reasoning capabilities. Temporal reasoning is an essential component of human cognition and understanding, reflecting the complexity of temporal expressions, logical implications, and integration with world knowledge.

Overview of TimeBench

TimeBench stands out with its comprehensive and hierarchical structure, which evaluates LLMs on various temporal reasoning tasks. Unlike prior studies that focus on isolated temporal aspects, TimeBench assesses three hierarchical levels of temporal reasoning: symbolic temporal reasoning, commonsense temporal reasoning, and event temporal reasoning.

1. Symbolic Temporal Reasoning: Evaluated through TimeX arithmetic and TimeX natural language inference (NLI), this level tests the understanding of abstract temporal expressions and their logical entailments.
2. Commonsense Temporal Reasoning: This level involves understanding world knowledge and commonsense principles through tasks such as MCTACO, DurationQA, TimeDial, and SituatedGen, focusing on event order, duration, and typicality.
3. Event Temporal Reasoning: Tasks like TimeQA, MenatQA, TempReason, and TRACIE test the temporal relationships between events, requiring models to reason under both explicit and implicit contexts.

Key Findings

The results from extensive evaluations of popular LLMs, including GPT-4, ChatGPT (GPT-3.5), LLaMA2, and others, have provided a clear picture of the current state of temporal reasoning in LLMs:

• Superior Performance but a Significant Gap: GPT-4 consistently demonstrates superior performance compared to other models, particularly in symbolic and commonsense reasoning tasks. However, it still lags significantly behind human performance, especially in complex event temporal reasoning scenarios, indicating that LLMs require further enhancements to achieve human-equivalent understanding.
• Alignment Effects: The alignment process, especially through techniques such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), shows a substantial impact on model performance. Models such as LLaMA2 and Mistral experienced performance degradation post-alignment, highlighting potential trade-offs in model optimization for diverse applications.
• Chain-of-Thought (CoT) Performance Variance: The application of CoT prompting does not universally improve temporal reasoning performance. While beneficial for symbolic reasoning, it often impairs performance in commonsense tasks. In event reasoning, CoT prompts provide inconsistent results, suggesting a nuanced effect dependent on task characteristics.

Implications and Future Directions

The introduction of TimeBench establishes a significant milestone in quantifying LLMs' temporal reasoning abilities, enabling targeted improvements. The observations regarding alignment-induced degradation and the mixed results of CoT prompting provide critical insights for future model training and design strategies.

Future research could focus on enhancing the inherent temporal reasoning skills of LLMs, possibly through specialized pre-training or integrating more sophisticated temporal knowledge bases. Additionally, exploring novel alignment strategies to balance conversational alignment without sacrificing core reasoning capabilities will be crucial.

In conclusion, TimeBench offers a robust framework to guide the development and refinement of LLMs, promoting advancements that bring these models closer to human-level temporal understanding. The findings of this paper lay a foundation for future explorations into temporal reasoning in AI, with implications that extend across various applications in natural language processing and beyond.