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Found 10 papers, 5 papers with code
PosterSum: A Multimodal Benchmark for Scientific Poster Summarization
We benchmark state-of-the-art Multimodal Large Language Models (MLLMs) on PosterSum and demonstrate that they struggle to accurately interpret and summarize scientific posters.
What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations
Transforming recorded videos into concise and accurate textual summaries is a growing challenge in multimodal learning.
Lost in Time: Clock and Calendar Understanding Challenges in Multimodal LLMs
Understanding time from visual representations is a fundamental cognitive skill, yet it remains a challenge for multimodal large language models (MLLMs).
End-to-End Long Document Summarization using Gradient Caching
Training transformer-based encoder-decoder models for long document summarization poses a significant challenge due to the quadratic memory consumption during training.
MovieSum: An Abstractive Summarization Dataset for Movie Screenplays
Movie screenplay summarization is challenging, as it requires an understanding of long input contexts and various elements unique to movies.
Are We Done with MMLU?
For example, we find that 57% of the analysed questions in the Virology subset contain errors.
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models
Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text.
Select and Summarize: Scene Saliency for Movie Script Summarization
Abstractive summarization for long-form narrative texts such as movie scripts is challenging due to the computational and memory constraints of current language models.
Data-Driven Compression of Convolutional Neural Networks
Designing and training a CNN architecture that does well on all three metrics is highly non-trivial and can be very time-consuming if done by hand.
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling
It is an emotion detection task on dialogues in the EmotionLines dataset.
