Search Results for author:
Found 9 papers, 6 papers with code
RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
By benchmarking RAG models in vertical domains, RAGEval has the ability to better evaluate the knowledge usage ability of LLMs, which avoids the confusion regarding the source of knowledge in answering question in existing QA datasets--whether it comes from parameterized memory or retrieval.
Autonomous Data Selection with Language Models for Mathematical Texts
Our method showcases a 2 times increase in pretraining token efficiency compared to state-of-the-art baselines, underscoring the potential of our approach in enhancing models' mathematical reasoning capabilities.
Augmenting Math Word Problems via Iterative Question Composing
The MMIQC dataset is available on the HuggingFace hub at https://huggingface. co/datasets/Vivacem/MMIQC.
Ranked #58 on
Math Word Problem Solving
on MATH
(using extra training data)
Prompt Engineering Through the Lens of Optimal Control
In this paper, we propose an optimal control framework tailored for multi-round interactions with LLMs.
GSLB: The Graph Structure Learning Benchmark
To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms.
Won't Get Fooled Again: Answering Questions with False Premises
In this paper, we find that the PLMs already possess the knowledge required to rebut such questions, and the key is how to activate the knowledge.
Double Descent of Discrepancy: A Task-, Data-, and Model-Agnostic Phenomenon
In this paper, we studied two identically-trained neural networks (i. e. networks with the same architecture, trained on the same dataset using the same algorithm, but with different initialization) and found that their outputs discrepancy on the training dataset exhibits a "double descent" phenomenon.
A Person Re-identification Data Augmentation Method with Adversarial Defense Effect
This method can not only improve the accuracy of the model, but also help the model defend against adversarial examples; 2) Multi-Modal Defense, it integrates three homogeneous modal images of visible, grayscale and sketch, and further strengthens the defense ability of the model.
Ranked #19 on
Person Re-Identification
on Market-1501-C
Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks
Federated learning (FL) in a bandwidth-limited network with energy-limited user equipments (UEs) is under-explored.
