100-LongBench: Are de facto Long-Context Benchmarks Literally Evaluating Long-Context Ability?
arXiv:2505.19293v2 Announce Type: replace-cross Abstract: Long-context capability is considered one of the most important abilities of LLMs, as a truly long context-capable LLM enables users to effortlessly process many originally exhausting tasks -- e.g., digesting a long-form document to find answers vs. directly asking an LLM about it. However, existing real-task-based long-context evaluation benchmarks have two major shortcomings. First, benchmarks like LongBench often do not provide proper metrics to separate long-context performance from the model's baseline ability, making cross-model c
This research is emerging as the capabilities and limitations of long-context LLMs become a critical area of focus for AI development and application.
Accurate evaluation of long-context ability is crucial for developing and deploying LLMs effectively, as current benchmarks may misrepresent actual capabilities.
The understanding of what constitutes true long-context capability in LLMs is shifting, requiring more sophisticated evaluation metrics beyond simple performance scores.
- · AI researchers focused on robust evaluation
- · Developers building real-world LLM applications
- · Companies investing in truly long-context capable models
- · Developers relying solely on existing, flawed benchmarks
- · LLM providers with superficially long-context models
Improvements in LLM evaluation methodologies will lead to more accurate assessments of model performance.
Better evaluation will drive the development of genuinely more capable long-context LLMs, enhancing their utility in complex tasks.
The broader adoption of these advanced LLMs could accelerate automation in fields requiring processing of extensive information.
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Read at arXiv cs.LG