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Found 16 papers, 10 papers with code
What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation
kNN-MT presents a new paradigm for domain adaptation by building an external datastore, which usually saves all target language token occurrences in the parallel corpus.
kNN-BOX: A Unified Framework for Nearest Neighbor Generation
Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT).
Question Translation Training for Better Multilingual Reasoning
A typical solution is to translate instruction data into all languages of interest, and then train on the resulting multilingual data, which is called translate-training.
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis
Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT).
Extrapolating Large Language Models to Non-English by Aligning Languages
We start from targeting individual languages by performing cross-lingual instruction-tuning (CoIT) on LLaMA, i. e. tuning it with translation task data and cross-lingual general task data to obtain cross-lingual models (x-LLaMAs), and formulate underlying scaling laws to investigate the advantages of using scalable translation data.
MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization
To enhance reasoning abilities in non-dominant languages, we propose a Multilingual-Alignment-as-Preference Optimization framework (MAPO), aiming to align the reasoning processes in other languages with the dominant language.
INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation
We propose an effective training framework INK to directly smooth the representation space via adjusting representations of kNN neighbors with a small number of new parameters.
Beyond Generic: Enhancing Image Captioning with Real-World Knowledge using Vision-Language Pre-Training Model
Considering that Vision-Language Pre-Training (VLP) models master massive such knowledge from large-scale web-harvested data, it is promising to utilize the generalizability of VLP models to incorporate knowledge into image descriptions.
Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation
To address the needs of learning representations for all languages in a unified space, we propose a novel efficient training recipe, upon which we build an effective detachable model, Lego-MT.
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios.
Gradient Episodic Memory with a Soft Constraint for Continual Learning
Catastrophic forgetting in continual learning is a common destructive phenomenon in gradient-based neural networks that learn sequential tasks, and it is much different from forgetting in humans, who can learn and accumulate knowledge throughout their whole lives.
Hierarchical Transformer for Scalable Graph Learning
Graph Transformer is gaining increasing attention in the field of machine learning and has demonstrated state-of-the-art performance on benchmarks for graph representation learning.
On Structural Expressive Power of Graph Transformers
Graph Transformer has recently received wide attention in the research community with its outstanding performance, yet its structural expressive power has not been well analyzed.
SIFTER: A Task-specific Alignment Strategy for Enhancing Sentence Embeddings
This indicates that different downstream tasks have different levels of sensitivity to sentence components.
PMNN:Physical Model-driven Neural Network for solving time-fractional differential equations
It establishes a temporal iteration scheme based on physical model-driven neural networks which effectively combines deep neural networks (DNNs) with interpolation approximation of fractional derivatives.
A parameter-free clustering algorithm for missing datasets
Missing datasets, in which some objects have missing values in certain dimensions, are prevalent in the Real-world.
