VideoWebArena: Evaluating Long Context Multimodal Agents with Video
  Understanding Web Tasks
Videos are often used to learn or extract the necessary information to
complete tasks in ways different than what text and static imagery alone can
provide. However, many existing agent benchmarks neglect long-context video
understanding, instead focusing on text or static image inputs. To bridge this
gap, we introduce VideoWebArena (VideoWA), a benchmark for evaluating the
capabilities of long-context multimodal agents for video understanding. VideoWA
consists of 2,021 web agent tasks based on manually crafted video tutorials,
which total almost four hours of content. For our benchmark, we define a
taxonomy of long-context video-based agent tasks with two main areas of focus:
skill retention and factual retention. While skill retention tasks evaluate
whether an agent can use a given human demonstration to complete a task
efficiently, the factual retention task evaluates whether an agent can retrieve
instruction-relevant information from a video to complete a task. We find that
the best model achieves 13.3% success on factual retention tasks and 45.8% on
factual retention QA pairs, far below human performance at 73.9% and 79.3%,
respectively. On skill retention tasks, long-context models perform worse with
tutorials than without, exhibiting a 5% performance decrease in WebArena tasks
and a 10.3% decrease in VisualWebArena tasks. Our work highlights the need to
improve the agentic abilities of long-context multimodal models and provides a
testbed for future development with long-context video agents.
672113b370842ddb5b173250
Lawrence Jang
claimed_verified
2024-12-20T08:34:16.391Z
664aebe829eadb3ab4e4ca3f
/avatars/548d5656e082c8959ac78b883f0805af.svg
Lawrence Jang
user
ljang0
672113b370842ddb5b173251
Yinheng Li
672113b370842ddb5b173252
Charles Ding
672113b370842ddb5b173253
Justin Lin
672113b370842ddb5b173254
Paul Pu Liang
672113b370842ddb5b173255
Dan Zhao
672113b370842ddb5b173256
Rogerio Bonatti
672113b370842ddb5b173257
Kazuhito Koishida
VideoWebArena: Evaluating Long Context Multimodal Agents with Video
  Understanding Web Tasks
Videos are often used to learn or extract the necessary information to
complete tasks in ways different than what text and static imagery alone can
provide. However, many existing agent benchmarks neglect long-context video
understanding, instead focusing on text or static image inputs. To bridge this
gap, we introduce VideoWebArena (VideoWA), a benchmark for evaluating the
capabilities of long-context multimodal agents for video understanding. VideoWA
consists of 2,021 web agent tasks based on manually crafted video tutorials,
which total almost four hours of content. For our benchmark, we define a
taxonomy of long-context video-based agent tasks with two main areas of focus:
skill retention and factual retention. While skill retention tasks evaluate
whether an agent can use a given human demonstration to complete a task
efficiently, the factual retention task evaluates whether an agent can retrieve
instruction-relevant information from a video to complete a task. We find that
the best model achieves 13.3% success on factual retention tasks and 45.8% on
factual retention QA pairs, far below human performance at 73.9% and 79.3%,
respectively. On skill retention tasks, long-context models perform worse with
tutorials than without, exhibiting a 5% performance decrease in WebArena tasks
and a 10.3% decrease in VisualWebArena tasks. Our work highlights the need to
improve the agentic abilities of long-context multimodal models and provides a
testbed for future development with long-context video agents.
A benchmark named VideoWebArena evaluates long-context video understanding in multimodal agents, showing that current models perform significantly worse than humans in both skill and factual retention tasks.
long-context multimodal agents video understanding VideoWebArena manual video tutorials skill retention factual retention agentic abilities long-context models WebArena VisualWebArena