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Found 7 papers, 1 papers with code
Tired of Plugins? Large Language Models Can Be End-To-End Recommenders
Recommender systems aim to predict user interest based on historical behavioral data.
Depth-induced Saliency Comparison Network for Diagnosis of Alzheimer's Disease via Jointly Analysis of Visual Stimuli and Eye Movements
Early diagnosis of Alzheimer's Disease (AD) is very important for following medical treatments, and eye movements under special visual stimuli may serve as a potential non-invasive biomarker for detecting cognitive abnormalities of AD patients.
Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges
We further present evaluation datasets used in existing methods, and finally conclude this survey by presenting the ongoing challenges and future directions.
Tuning Large language model for End-to-end Speech Translation
The training of LST consists of two stages: (1) Modality adjustment, where the adapter is tuned to align speech representation with text embedding space, and (2) Downstream task fine-tuning, where both the adapter and LLM model are trained to optimize performance on the E2EST task.
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations
Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task-specific perturbations, e. g., replacing key question entities or shuffling table columns.
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning
However, the final model often performs worse on the MT task than the MT model trained alone, which means that the knowledge transfer ability of this method is also limited.
Decouple Non-parametric Knowledge Distillation For End-to-end Speech Translation
Existing techniques often attempt to make knowledge transfer from a powerful machine translation (MT) to speech translation (ST) model with some elaborate techniques, which often requires transcription as extra input during training.
