LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams (2504.17366v1)

Abstract: Long-context understanding poses significant challenges in natural language processing, particularly for real-world dialogues characterized by speech-based elements, high redundancy, and uneven information density. Although LLMs achieve impressive results on existing benchmarks, these datasets fail to reflect the complexities of such texts, limiting their applicability to practical scenarios. To bridge this gap, we construct the first spoken long-text dataset, derived from live streams, designed to reflect the redundancy-rich and conversational nature of real-world scenarios. We construct tasks in three categories: retrieval-dependent, reasoning-dependent, and hybrid. We then evaluate both popular LLMs and specialized methods to assess their ability to understand long-contexts in these tasks. Our results show that current methods exhibit strong task-specific preferences and perform poorly on highly redundant inputs, with no single method consistently outperforming others. We propose a new baseline that better handles redundancy in spoken text and achieves strong performance across tasks. Our findings highlight key limitations of current methods and suggest future directions for improving long-context understanding. Finally, our benchmark fills a gap in evaluating long-context spoken language understanding and provides a practical foundation for developing real-world e-commerce systems. The code and benchmark are available at https://github.com/Yarayx/livelongbench.

LiveLongBench: Advancing Long-Context Understanding in Spoken Language

The paper "LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams" introduces an innovative and comprehensive benchmark designed to address the intricate challenges associated with long-context understanding in NLP, particularly within the domain of live-streaming spoken texts. The authors present this benchmark as a vital tool for assessing the capabilities of LLMs and specialized methodologies in processing and understanding verbose and often redundant spoken language that characterizes real-world dialogues.

Core Contributions

The research primarily contributes by constructing LiveLongBench, the first dataset focused explicitly on spoken long-text understanding derived from live streams. This dataset is distinctive because it mirrors the redundancy-rich and conversational nature inherent in real-world spoken scenarios, setting it apart from existing benchmarks that predominantly emphasize written language. The dataset facilitates task evaluation across three categories: retrieval-dependent, reasoning-dependent, and hybrid tasks, thus offering a multifaceted view of model performance.

Numerical Results and Claims

The authors evaluate several popular LLMs and context compression methods against LiveLongBench to gauge their effectiveness. Their findings reveal that current methods display pronounced task-specific preferences and often perform inefficiently on highly redundant inputs, although no single method consistently excels across all tasks. Notably, the introduction of a new baseline that effectively handles redundancy in spoken texts results in robust performance improvements across diverse tasks. This highlights significant limitations in current approaches and pinpoints areas for further enhancement, encouraging the development of more efficient strategies capable of managing spoken text complexities.

Implications and Future Developments

Practically, LiveLongBench serves as a pivotal foundation for advancing the understanding of spoken languages, crucial for real-world e-commerce systems where spoken texts are prevalent. It presents a critical avenue for the development of LLMs capable of handling the nuanced dynamics of live-streaming environments, characterized by high information redundancy and varying information density.

Theoretically, these findings emphasize the potential of hybrid systems that intelligently combine retrieval and reasoning capabilities to optimize understanding long-context scenarios. There is a stark recognition of the need for neural models that incorporate efficient redundancy management techniques, such as advanced key-value cache compression strategies, which could offer significant gains in both performance and computational resource management.

Speculations on AI Developments

As LLMs continue to evolve, the insights gleaned from LiveLongBench can significantly inform the architectural advancements required to handle multi-lingual, multi-modal, long-context understanding tasks. Future advancements might include refined model architectures that integrate efficient context length processing with domain-specific adaptations to enhance the robustness and reliability of AI systems in live interaction contexts. This paper sets a precedent for subsequent research focused on overcoming the inherent challenges posed by spoken language understanding, fostering the development of more nuanced AI systems designed for real-time, long-form conversational contexts.

In conclusion, "LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams" not only fills a critical evaluation gap for long-context spoken language processing but also lays the groundwork for further research aimed at overcoming the unique challenges posed by spoken language. It encourages continued exploration of novel techniques to address redundancy and ensures that future LLMs are well-equipped to handle complex, real-world scenarios efficiently.