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Found 14 papers, 8 papers with code
Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models
Subsequently, ESREAL computes token-level hallucination scores by assessing the semantic similarity of aligned regions based on the type of hallucination.
Gradient Estimation for Unseen Domain Risk Minimization with Pre-Trained Models
Although the task-specific knowledge could be learned from source domains by fine-tuning, this hurts the generalization power of pre-trained models due to gradient bias toward the source domains.
TiDAL: Learning Training Dynamics for Active Learning
Active learning (AL) aims to select the most useful data samples from an unlabeled data pool and annotate them to expand the labeled dataset under a limited budget.
Measuring and Improving Semantic Diversity of Dialogue Generation
In this paper, we introduce a new automatic evaluation metric to measure the semantic diversity of generated responses.
Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild
In this study, we formulate real-world scenarios of content moderation and introduce a simple yet effective threshold optimization method that searches the optimal thresholds of the multiple subtasks to make a reliable moderation decision in a cost-effective way.
Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances
To better reflect the style of the character, PDP builds the prompts in the form of dialog that includes the character's utterances as dialog history.
Understanding and Improving the Exemplar-based Generation for Open-domain Conversation
Extensive experiments demonstrate that our proposed training method alleviates the drawbacks of the existing exemplar-based generative models and significantly improves the performance in terms of appropriateness and informativeness.
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset.
Temporal Knowledge Distillation for On-device Audio Classification
Improving the performance of on-device audio classification models remains a challenge given the computational limits of the mobile environment.
Ranked #7 on
Audio Classification
on FSD50K
Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation
G2R consists of two distinct techniques of distillation: the data-level G2R augments the dialogue dataset with additional responses generated by the large-scale generative model, and the model-level G2R transfers the response quality score assessed by the generative model to the score of the retrieval model by the knowledge distillation loss.
Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learning
Despite the rapid growth of online advertisement in developing countries, existing highly over-parameterized Click-Through Rate (CTR) prediction models are difficult to be deployed due to the limited computing resources.
``Killing Me'' Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites.
"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites.
Disentangling Label Distribution for Long-tailed Visual Recognition
Although this method surpasses state-of-the-art methods on benchmark datasets, it can be further improved by directly disentangling the source label distribution from the model prediction in the training phase.
Ranked #20 on
Long-tail Learning
on Places-LT
