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Found 23 papers, 14 papers with code
Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning
We propose explanation-consistency finetuning (EC-finetuning), a method that adapts LLMs to generate more consistent natural-language explanations on related examples.
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
We conduct extensive experiments in both event type prediction and uncertainty quantification of arrival time.
Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback.
DeepTagger: Knowledge Enhanced Named Entity Recognition for Web-Based Ads Queries
For model-free enhancement, we collect unlabeled web queries to augment domain knowledge; and we collect web search results to enrich the information of ads queries.
Machine Learning Force Fields with Data Cost Aware Training
To address this issue, we propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
Efficient Long Sequence Modeling via State Space Augmented Transformer
Specifically, we augment a SSM into the bottom layer of SPADE, and we employ efficient local attention methods for the other layers.
Less is More: Task-aware Layer-wise Distillation for Language Model Compression
As such, TED reduces the knowledge gap between the two models and helps the student to fit better on the target task.
DiP-GNN: Discriminative Pre-Training of Graph Neural Networks
Specifically, we train a generator to recover identities of the masked edges, and simultaneously, we train a discriminator to distinguish the generated edges from the original graph's edges.
Differentially Private Estimation of Hawkes Process
Point process models are of great importance in real world applications.
Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites
The model subsequently calculates session representations by combining the contextual information with the instant search query using an aggregation network.
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
Large Transformer-based models have exhibited superior performance in various natural language processing and computer vision tasks.
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation
We propose MoEBERT, which uses a Mixture-of-Experts structure to increase model capacity and inference speed.
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models
Analysis shows that the proposed schedule indeed reduces the redundancy and improves generalization performance.
Taming Sparsely Activated Transformer with Stochastic Experts
While most on-going research focuses on improving SAMs models by exploring methods of routing inputs to experts, our analysis reveals that such research might not lead to the solution we expect, i. e., the commonly-used routing methods based on gating mechanisms do not work better than randomly routing inputs to experts.
Adversarially Regularized Policy Learning Guided by Trajectory Optimization
Recent advancement in combining trajectory optimization with function approximation (especially neural networks) shows promise in learning complex control policies for diverse tasks in robot systems.
ARCH: Efficient Adversarial Regularized Training with Caching
Adversarial regularization can improve model generalization in many natural language processing tasks.
Self-Training with Differentiable Teacher
In self-training, the student contributes to the prediction performance, and the teacher controls the training process by generating pseudo-labels.
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization
The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ``lottery tickets'', and training a certain collection of them (i. e., a subnetwork) can match the performance of the full model.
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach
Adversarial regularization has been shown to improve the generalization performance of deep learning models in various natural language processing tasks.
A Hypergradient Approach to Robust Regression without Correspondence
Due to the combinatorial nature of the problem, most existing methods are only applicable when the sample size is small, and limited to linear regression models.
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision.
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Transformer Hawkes Process
Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets.
Image Score: How to Select Useful Samples
There has long been debates on how we could interpret neural networks and understand the decisions our models make.
