Search Results for author:
Found 35 papers, 26 papers with code
A Language Anchor-Guided Method for Robust Noisy Domain Generalization
Noisy data worsens this problem by causing further overfitting to the noise, meaning that existing methods often fail to tell the difference between true, invariant features and misleading, spurious ones.
StepMathAgent: A Step-Wise Agent for Evaluating Mathematical Processes through Tree-of-Error
Evaluating mathematical capabilities is critical for assessing the overall performance of large language models (LLMs).
Outcome-Refining Process Supervision for Code Generation
Large Language Models have demonstrated remarkable capabilities in code generation, yet they often struggle with complex programming tasks that require deep algorithmic reasoning.
Learning from "Silly" Questions Improves Large Language Models, But Only Slightly
In addition, for specific tasks, different rules tend to have a consistent impact on model performance.
On the Diversity of Synthetic Data and its Impact on Training Large Language Models
The rise of Large Language Models (LLMs) has accentuated the need for diverse, high-quality pre-training data.
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
Leveraging RAGLAB, we conduct a fair comparison of 6 RAG algorithms across 10 benchmarks.
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
Through in-depth understanding of the latest advancements and practical applications, this survey provides valuable resources for researchers, paving the way for sustained progress in this field.
Enhancing In-Context Learning via Implicit Demonstration Augmentation
The emergence of in-context learning (ICL) enables large pre-trained language models (PLMs) to make predictions for unseen inputs without updating parameters.
AutoSurvey: Large Language Models Can Automatically Write Surveys
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence.
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models
The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-effectively while ensuring reliability, reproducibility, and efficiency.
Apollo: A Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People
Despite the vast repository of global medical knowledge predominantly being in English, local languages are crucial for delivering tailored healthcare services, particularly in areas with limited medical resources.
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness.
A General Framework for Learning from Weak Supervision
Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algorithms, thereby hindering the practical deployment.
Supervised Knowledge Makes Large Language Models Better In-context Learners
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering.
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language Models (LLMs).
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences.
PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Furthermore, we present a comprehensive analysis to understand the mystery behind prompt robustness and its transferability.
Cross-Lingual Paraphrase Identification
Machine Translation
+5
Out-of-Distribution Generalization in Text Classification: Past, Present, and Future
Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differs from the training data distribution.
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
In this paper, we introduce imprecise label learning (ILL), a framework for the unification of learning with various imprecise label configurations.
Ranked #1 on
Learning with noisy labels
on mini WebVision 1.0
Evaluating Open-QA Evaluation
This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs).
Exploring Vision-Language Models for Imbalanced Learning
However, their performance on imbalanced dataset is relatively poor, where the distribution of classes in the training dataset is skewed, leading to poor performance in predicting minority classes.
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective.
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.
An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
While standard SSL assumes uniform data distribution, we consider a more realistic and challenging setting called imbalanced SSL, where imbalanced class distributions occur in both labeled and unlabeled data.
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase.
Natural Language Understanding
Out-of-Distribution Generalization
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
For optimization, we utilize an adapted Mixup to generate an out-of-distribution dataset that can guide the preference direction and optimize with Pareto optimization.
Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction
Limited labeled data increase the risk of distribution shift between test data and training data.
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
Margin Calibration for Long-Tailed Visual Recognition
The long-tailed class distribution in visual recognition tasks poses great challenges for neural networks on how to handle the biased predictions between head and tail classes, i. e., the model tends to classify tail classes as head classes.
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learning status and learning difficulties of different classes.
Exploiting Adapters for Cross-lingual Low-resource Speech Recognition
Based on our previous MetaAdapter that implicitly leverages adapters, we propose a novel algorithms called SimAdapter for explicitly learning knowledge from adapters.
Ranked #1 on
Cross-Lingual ASR
on Common Voice
Unsupervised segmentation via semantic-apparent feature fusion
In order to solve this problem, the research proposes an unsupervised foreground segmentation method based on semantic-apparent feature fusion (SAFF).
