4 papers
SagaQA: A Multi-hop Reasoning Benchmark for Long-form Narrative Understanding in TV Series
Galann Pennec, Zhengyuan Liu, Nicholas Asher +2
We introduce SagaQA, a long-form video benchmark for multi-hop reasoning over full-length TV series. Existing video reasoning benchmarks often emphasize local understanding of adja…
Minimal Clips, Maximum Salience: Long Video Summarization via Key Moment Extraction
Galann Pennec, Zhengyuan Liu, Nicholas Asher +2
Vision-Language Models (VLMs) are able to process increasingly longer videos. Yet, important visual information is easily lost throughout the entire context and missed by VLMs. Als…
Integrating Video and Text: A Balanced Approach to Multimodal Summary Generation and Evaluation
Galann Pennec, Zhengyuan Liu, Nicholas Asher +2
Vision-Language Models (VLMs) often struggle to balance visual and textual information when summarizing complex multimodal inputs, such as entire TV show episodes. In this paper, w…
In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Models
Ayrton San Joaquin, Bin Wang, Zhengyuan Liu +4
Despite advancements, fine-tuning Large Language Models (LLMs) remains costly due to the extensive parameter count and substantial data requirements for model generalization. Acces…