Typicalness-A ware Learning (T AL) to address this issue and improve failure detection performance.

The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query.

In this work, we propose a novel calibration method that can be used to combat hallucinations.

Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks.

We propose two training-free context window extension methods via the lens of adjusting positional vectors, i.e., positional vector replacement and attention window extension.

Ti-FAD outperforms the state-of-the-art methods on ZST AD benchmarks by a large margin: 41.2% (+ 11.0%) on THUMOS14 and 32.0% (+ 5.4%) on ActivityNet v1.3. Code is available at: https://github.com/Y

As language models continue to scale, Large Language Models (LLMs) have exhibited emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks by prefixing a few in-context demonstrations (ICDs) as context. Inspired by these advancements, researchers have extended these techniques to develop Large Multimodal Models (LMMs) with ICL capabilities.