Search Results for author:
Found 17 papers, 6 papers with code
Can Data Diversity Enhance Learning Generalization?
This paper introduces our Diversity Advanced Actor-Critic reinforcement learning (A2C) framework (DAAC) to improve the generalization and accuracy of Natural Language Processing (NLP).
Measuring Robustness for NLP
The quality of Natural Language Processing (NLP) models is typically measured by the accuracy or error rate of a predefined test set.
Nimbus: Secure and Efficient Two-Party Inference for Transformers
For the linear layer, we propose a new 2PC paradigm along with an encoding approach to securely compute matrix multiplications based on an outer-product insight, which achieves $2. 9\times \sim 12. 5\times$ performance improvements compared to the state-of-the-art (SOTA) protocol.
Learning Code Preference via Synthetic Evolution
and (ii) How do human and LLM preferences align with verifiable code properties and developer code tastes?
Xinyu: An Efficient LLM-based System for Commentary Generation
To address the advanced requirements, we present an argument ranking model for arguments and establish a comprehensive evidence database that includes up-to-date events and classic books, thereby strengthening the substantiation of the evidence with retrieval augmented generation (RAG) technology.
$\text{Memory}^3$: Language Modeling with Explicit Memory
The model is named $\text{Memory}^3$, since explicit memory is the third form of memory in LLMs after implicit memory (model parameters) and working memory (context key-values).
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models
Large language models (LLMs) excel in generating coherent text, but they often struggle with context awareness, leading to inaccuracies in tasks requiring faithful adherence to provided information.
Knowledge Graph Pruning for Recommendation
Recent years have witnessed the prosperity of knowledge graph based recommendation system (KGRS), which enriches the representation of users, items, and entities by structural knowledge with striking improvement.
Investigating Training Strategies and Model Robustness of Low-Rank Adaptation for Language Modeling in Speech Recognition
The use of low-rank adaptation (LoRA) with frozen pretrained language models (PLMs) has become increasing popular as a mainstream, resource-efficient modeling approach for memory-constrained hardware.
HuRef: HUman-REadable Fingerprint for Large Language Models
In this study, we introduce HuRef, a human-readable fingerprint for LLMs that uniquely identifies the base model without interfering with training or exposing model parameters to the public.
Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition
We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring.
Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the overdetected false spans at the span detection stage and the inaccurate and unstable prototypes at the type classification stage remain to be challenging problems.
Ranked #2 on
Few-shot NER
on Few-NERD (INTRA)
Using AntiPatterns to avoid MLOps Mistakes
We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications.
The Weak Lensing Peak Statistics in the Mocks by the inverse-Gaussianization Method
We apply the inverse-Gaussianization method proposed in \citealt{arXiv:1607. 05007} to fast produce weak lensing convergence maps and investigate the peak statistics, including the peak height counts and peak steepness counts, in these mocks.
Cosmology and Nongalactic Astrophysics
Unsupervised Representation Learning for Gaze Estimation
Although automatic gaze estimation is very important to a large variety of application areas, it is difficult to train accurate and robust gaze models, in great part due to the difficulty in collecting large and diverse data (annotating 3D gaze is expensive and existing datasets use different setups).
Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis
In this work, we address the problem of person-specific gaze model adaptation from only a few reference training samples.
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image.
