ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains (2410.09870v3)

Abstract: LLMs have brought significant changes to many aspects of our lives. However, assessing and ensuring their chronological knowledge remains challenging. Existing approaches fall short in addressing the temporal adaptability of knowledge, often relying on a fixed time-point view. To overcome this, we introduce ChroKnowBench, a benchmark dataset designed to evaluate chronologically accumulated knowledge across three key aspects: multiple domains, time dependency, temporal state. Our benchmark distinguishes between knowledge that evolves (e.g., personal history, scientific discoveries, amended laws) and knowledge that remain constant (e.g., mathematical truths, commonsense facts). Building on this benchmark, we present ChroKnowledge (Chronological Categorization of Knowledge), a novel sampling-based framework for evaluating LLMs' non-parametric chronological knowledge. Our evaluation led to the following observations: (1) The ability of eliciting temporal knowledge varies depending on the data format that model was trained on. (2) LLMs partially recall knowledge or show a cut-off at temporal boundaries rather than recalling all aspects of knowledge correctly. Thus, we apply our ChroKnowPrompt, an in-depth prompting to elicit chronological knowledge by traversing step-by-step through the surrounding time spans. We observe that it successfully recalls objects across both open-source and proprietary LLMs, demonstrating versatility, though it faces challenges with dynamic datasets and unstructured formats.

• The paper introduces a framework and a dedicated dataset (ChroKnowBench) to evaluate and differentiate between static and dynamic knowledge in LLMs.
• The methodology uses ChroKnowPrompt’s iterative approach, achieving significant improvements such as an 11.9% boost in biomedical knowledge recall.
• The findings underscore the importance of temporal context in LLMs, suggesting future integration of hybrid update techniques for enhanced accuracy.

Insights into "ChroKnowledge: Unveiling Chronological Knowledge of LLMs in Multiple Domains"

The paper "ChroKnowledge: Unveiling Chronological Knowledge of LLMs in Multiple Domains" addresses the critical challenge of ensuring that LLMs accurately track and update knowledge over time. The authors have proposed a novel framework, ChroKnowledge, which is complemented by a benchmarking dataset called ChroKnowBench. The primary focus is on evaluating the chronological knowledge of LLMs across multiple domains, emphasizing the accumulative and evolving nature of knowledge.

Key Contributions

The paper introduces several significant contributions:

1. ChroKnowBench Dataset: This dataset is designed to systematically evaluate the chronological knowledge of LLMs. It encompasses multiple domains such as general, biomedical, legal, commonsense, and mathematics. The dataset delineates time-variant knowledge, which evolves, from time-invariant knowledge, which remains constant over time.
2. ChroKnowledge Framework: A key innovation of the paper is the ChroKnowledge framework, which employs a sampling-based approach to assess and update non-parametric chronological knowledge within LLMs. It differentiates between static knowledge that remains unchanged and dynamic knowledge that evolves over time, offering a perspicacious methodology for evaluating time-dependent knowledge.
3. ChroKnowPrompt: To address the limitation of partial recall in LLMs, the authors introduce ChroKnowPrompt, an iterative prompting technique. This approach effectively updates models by traversing through temporal spans to enhance temporal knowledge representation.

Findings

The research reveals that the ability of LLMs to recall and update temporal knowledge is significantly influenced by the format of the data they were trained on. Intriguingly, different domains exhibit varied patterns in maintaining dynamic and static knowledge, with biomedical and general domains demonstrating noteworthy characteristics:

• In the biomedical domain, the framework achieved a substantial improvement of 11.9% in knowledge recall, underpinning the efficacy of the ChroKnowPrompt in domains where recent and historical knowledge integration is pivotal.
• In the general domain, a modest improvement of 2.8% was observed. This highlights the ongoing challenge of updating non-parametric knowledge in more composite domains.

Implications and Future Directions

The findings underscore the importance of incorporating temporal context in LLMs to enhance their relevance and reliability. This work has broad implications for various applications, from scientific research updates to dynamic legal regulation tracking. By enabling non-parametric updates, the framework accommodates both open-source and proprietary LLMs, allowing for extensive applicability.

Future research may explore integrating parametric methods with ChroKnowledge to enrich knowledge update mechanisms further. Exploring a hybrid approach that combines parametric and non-parametric updates could offer more precise alignment of knowledge across time scales. Additionally, expanding the temporal scope and refining the prompts could further enhance the accuracy and breadth of chronological knowledge in LLMs.

Conclusion

This research provides a rigorous framework to tackle the complexities of temporal knowledge in LLMs. It highlights the nuanced interplay between domain-specific characteristics and the evolving nature of knowledge. As the field progresses, the methodologies developed in this paper offer vital insights and tools to refine and advance the temporal reasoning capabilities of AI models.