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Found 17 papers, 5 papers with code
Improving Language Model Reasoning with Self-motivated Learning
The framework motivates the model itself to automatically generate rationales on existing datasets.
Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities
With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably.
PROXYQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models
LLMs are prompted to generate extensive content in response to these meta-questions.
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding
Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e. g., intents and slots).
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template.
A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification
Cross-domain text classification aims to adapt models to a target domain that lacks labeled data.
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly changing the essential semantics of the original image.
HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser
This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing.
A Framework of Learning Through Empirical Gain Maximization
Furthermore, it is shown that several well-known robust nonconvex regression paradigms, such as Tukey regression and truncated least square regression, can be reformulated into this new framework.
A Statistical Learning Assessment of Huber Regression
Third, with an adaptive choice of the scale parameter, we demonstrate that Huber regression estimators can be asymptotic mean regression calibrated under $(1+\epsilon)$-moment conditions ($\epsilon>0$).
N-LTP: An Open-source Neural Language Technology Platform for Chinese
We introduce \texttt{N-LTP}, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: {lexical analysis} (Chinese word segmentation, part-of-speech tagging, and named entity recognition), {syntactic parsing} (dependency parsing), and {semantic parsing} (semantic dependency parsing and semantic role labeling).
New Insights into Learning with Correntropy Based Regression
Third, we present some new results when it is utilized to learn the conditional mean function by developing its error bounds and exponential convergence rates under conditional $(1+\epsilon)$-moment assumptions.
Half-Quadratic Alternating Direction Method of Multipliers for Robust Orthogonal Tensor Approximation
In this paper, based on the maximum a posterior estimation, we derive a robust orthogonal tensor CPD model with Cauchy loss, which is resistant to heavy-tailed noise or outliers.
Optimization and Control
Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise
Motivated by the practical way of generating non-Gaussian noise or outliers, we introduce mixture of symmetric stable noise, which include Gaussian noise, Cauchy noise, and their mixture as special cases, to model non-Gaussian noise or outliers.
A Statistical Learning Approach to Modal Regression
However, it outperforms these regression models in terms of robustness as shown in our study from a re-descending M-estimation view.
Kernel Density Estimation for Dynamical Systems
We study the density estimation problem with observations generated by certain dynamical systems that admit a unique underlying invariant Lebesgue density.
Learning theory estimates with observations from general stationary stochastic processes
We show that when the stochastic processes satisfy a generalized Bernstein-type inequality, a unified treatment on analyzing the learning schemes with various mixing processes can be conducted and a sharp oracle inequality for generic regularized empirical risk minimization schemes can be established.
