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Found 19 papers, 5 papers with code
Integrating Planning into Single-Turn Long-Form Text Generation
To overcome the scarcity of training data for these intermediate steps, we leverage LLMs to generate synthetic intermediate writing data such as outlines, key information and summaries from existing full articles.
$M^3EL$: A Multi-task Multi-topic Dataset for Multi-modal Entity Linking
Multi-modal Entity Linking (MEL) is a fundamental component for various downstream tasks.
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild
As Large Language Models (LLMs) excel across tasks and specialized domains, scaling LLMs based on existing models has garnered significant attention, which faces the challenge of decreasing performance when combining disparate models.
Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation
Unlike traditional methods, which generate two responses before obtaining the preference label, RMBoost first generates one response and selects a preference label, followed by generating the second more (or less) preferred response conditioned on the pre-selected preference label and the first response.
Online Drift Detection with Maximum Concept Discrepancy
Continuous learning from an immense volume of data streams becomes exceptionally critical in the internet era.
Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors
Continual Few-shot Relation Extraction (CFRE) is a practical problem that requires the model to continuously learn novel relations while avoiding forgetting old ones with few labeled training data.
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling
Intrusive PEFT techniques directly change a model's internal architecture.
Automated Evaluation of Personalized Text Generation using Large Language Models
Personalized text generation presents a specialized mechanism for delivering content that is specific to a user's personal context.
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal model size.
AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation
Unlike typical adaptive computation challenges that deal with single-step generation problems, diffusion processes with a multi-step generation need to dynamically adjust their computational resource allocation based on the ongoing assessment of each step's importance to the final image output, presenting a unique set of challenges.
Teach LLMs to Personalize -- An Approach inspired by Writing Education
Inspired by the practice of writing education, we develop a multistage and multitask framework to teach LLMs for personalized generation.
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model
To handle this challenge, we propose a novel early exiting strategy for unified visual language models, which allows dynamically skip the layers in encoder and decoder simultaneously in term of input layer-wise similarities with multiple times of early exiting, namely \textbf{MuE}.
Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization
For example, even in the commonly adopted instructional videos, a speaker can sometimes refer to something that is not visually present in the current frame; and the semantic misalignment would only be more unpredictable for the raw videos from the internet.
Self-supervised Domain Adaptation in Crowd Counting
Self-training crowd counting has not been attentively explored though it is one of the important challenges in computer vision.
Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)
Few-shot relation learning refers to infer facts for relations with a limited number of observed triples.
COVID-19 Forecasts via Stock Market Indicators
Reliable short term forecasting can provide potentially lifesaving insights into logistical planning, and in particular, into the optimal allocation of resources such as hospital staff and equipment.
Optimal Function Approximation with Relu Neural Networks
We consider in this paper the optimal approximations of convex univariate functions with feed-forward Relu neural networks.
Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field
To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two common strategies, namely the spatial-spectral information integration and the utilization of neural networks.
Ranked #8 on
Hyperspectral Image Classification
on Indian Pines
(Overall Accuracy metric)
General Classification
Hyperspectral Image Classification
+4
When CTC Training Meets Acoustic Landmarks
In this paper, the convergence properties of CTC are improved by incorporating acoustic landmarks.
