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Found 12 papers, 11 papers with code
Translation as Cross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-Domain Segmenting and Labeling Tasks
The nature of no word delimiter or inflection that can indicate segment boundaries or word semantics increases the difficulty of Chinese text understanding, and also intensifies the demand for word-level semantic knowledge to accomplish the tagging goal in Chinese segmenting and labeling tasks.
Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality
Video paragraph captioning (VPC) involves generating detailed narratives for long videos, utilizing supportive modalities such as speech and event boundaries.
TempCompass: Do Video LLMs Really Understand Videos?
Motivated by these two problems, we propose the \textbf{TempCompass} benchmark, which introduces a diversity of temporal aspects and task formats.
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
We first formulate a general framework of agent backdoor attacks, then we present a thorough analysis on the different forms of agent backdoor attacks.
RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge
Therefore, to evaluate the ability of LLMs to discern the reliability of external knowledge, we create a benchmark from existing knowledge bases.
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
The multi-aspect categorization of FETV enables fine-grained analysis of the metrics' reliability in different scenarios.
TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
TESTA can reduce the number of visual tokens by 75% and thus accelerate video encoding.
Ranked #1 on
Video Retrieval
on Condensed Movies
(using extra training data)
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter
However, as pre-trained language models (PLMs) continue to increase in size, the communication cost for transmitting parameters during synchronization has become a training speed bottleneck.
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features
We find that: (1) no existing method behaves well in both settings; (2) fine-tuning PLMs on in-distribution data benefits detecting semantic shifts but severely deteriorates detecting non-semantic shifts, which can be attributed to the distortion of task-agnostic features.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Holistic Sentence Embeddings for Better Out-of-Distribution Detection
On the basis of the observations that token averaging and layer combination contribute to improving OOD detection, we propose a simple embedding approach named Avg-Avg, which averages all token representations from each intermediate layer as the sentence embedding and significantly surpasses the state-of-the-art on a comprehensive suite of benchmarks by a 9. 33% FAR95 margin.
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks
In this work, we take the first step to investigate the unconcealment of textual poisoned samples at the intermediate-feature level and propose a feature-based efficient online defense method.
Feature Space Singularity for Out-of-Distribution Detection
Our analysis of the phenomenon reveals why our algorithm works.
Ranked #1 on
Out-of-Distribution Detection
on MS-1M vs. IJB-C
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
