  Large multimodal models (LMMs) are processing increasingly longer and richer
inputs. Albeit the progress, few public benchmark is available to measure such
development. To mitigate this gap, we introduce LongVideoBench, a
question-answering benchmark that features video-language interleaved inputs up
to an hour long. Our benchmark includes 3,763 varying-length web-collected
videos with their subtitles across diverse themes, designed to comprehensively
evaluate LMMs on long-term multimodal understanding. To achieve this, we
interpret the primary challenge as to accurately retrieve and reason over
detailed multimodal information from long inputs. As such, we formulate a novel
video question-answering task termed referring reasoning. Specifically, as part
of the question, it contains a referring query that references related video
contexts, called referred context. The model is then required to reason over
relevant video details from the referred context. Following the paradigm of
referring reasoning, we curate 6,678 human-annotated multiple-choice questions
in 17 fine-grained categories, establishing one of the most comprehensive
benchmarks for long-form video understanding. Evaluations suggest that the
LongVideoBench presents significant challenges even for the most advanced
proprietary models (e.g. GPT-4o, Gemini-1.5-Pro, GPT-4-Turbo), while their
open-source counterparts show an even larger performance gap. In addition, our
results indicate that model performance on the benchmark improves only when
they are capable of processing more frames, positioning LongVideoBench as a
valuable benchmark for evaluating future-generation long-context LMMs.
