Search Results for author:
Found 16 papers, 5 papers with code
XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks.
Ploutos: Towards interpretable stock movement prediction with financial large language model
Recent advancements in large language models (LLMs) have opened new pathways for many domains.
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers
This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing.
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
This indicates that crafting multilingual corpora can be regarded as a vital strategy for enhancing model performance in a specific language, especially in mathematical reasoning tasks.
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
RUEL is the first method that connects user browsing data with typical recommendation datasets and can be generalized to various recommendation scenarios and datasets.
HGCC: Enhancing Hyperbolic Graph Convolution Networks on Heterogeneous Collaborative Graph for Recommendation
Due to the naturally power-law distributed nature of user-item interaction data in recommendation tasks, hyperbolic space modeling has recently been introduced into collaborative filtering methods.
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval
However, we find that the performance of the cross-encoder re-ranker is heavily influenced by the number of training samples and the quality of negative samples, which is hard to obtain in the cross-lingual setting.
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval
In this paper, we propose to mine and generate self-supervised training data based on a large-scale unlabeled corpus.
Large Language Models are Diverse Role-Players for Summarization Evaluation
First, we propose to model objective and subjective dimensions of generated text based on roleplayers prompting mechanism.
Indeterminate Probability Neural Network
Besides, for our proposed neural network framework, the output of neural network is defined as probability events, and based on the statistical analysis of these events, the inference model for classification task is deduced.
Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
On the multilingual sentence retrieval task Tatoeba, our model achieves new SOTA results among methods without using bilingual data.
On the universality of AdS$_2$ diffusion bounds and the breakdown of linearized hydrodynamics
The chase of universal bounds on diffusivities in strongly coupled systems and holographic models has a long track record.
High Energy Physics - Theory Strongly Correlated Electrons
Note on a less ad hoc derivation of the second quantization formalism
Second quantization is an essential topic in senior undergraduate and postgraduate level Quantum Mechanics course.
Quantization
Quantum Physics
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks.
Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation
To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task.
