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Found 11 papers, 7 papers with code
Enhance Mobile Agents Thinking Process Via Iterative Preference Learning
The Chain of Action-Planning Thoughts (CoaT) paradigm has been shown to improve the reasoning performance of VLM-based mobile agents in GUI tasks.
Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study
Large language models (LLMs) have shown continuously improving multilingual capabilities, and even small-scale open-source models have demonstrated rapid performance enhancement.
Mixture of Diverse Size Experts
The Sparsely-Activated Mixture-of-Experts (MoE) has gained increasing popularity for scaling up large language models (LLMs) without exploding computational costs.
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
This study focuses on the topic of LLMs assist NLP Researchers, particularly examining the effectiveness of LLM in assisting paper (meta-)reviewing and its recognizability.
Towards Boosting Many-to-Many Multilingual Machine Translation with Large Language Models
The training paradigm for machine translation has gradually shifted, from learning neural machine translation (NMT) models with extensive parallel corpora to instruction finetuning on multilingual large language models (LLMs) with high-quality translation pairs.
An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation
Consistency regularization methods, such as R-Drop (Liang et al., 2021) and CrossConST (Gao et al., 2023), have achieved impressive supervised and zero-shot performance in the neural machine translation (NMT) field.
Learning Multilingual Sentence Representations with Cross-lingual Consistency Regularization
Multilingual sentence representations are the foundation for similarity-based bitext mining, which is crucial for scaling multilingual neural machine translation (NMT) system to more languages.
Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization
The experimental analysis also proves that CrossConST could close the sentence representation gap and better align the representation space.
Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation
We introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance.
Ranked #1 on
Machine Translation
on WMT2014 German-English
Mixup Decoding for Diverse Machine Translation
To further improve the faithfulness and diversity of the translations, we propose two simple but effective approaches to select diverse sentence pairs in the training corpus and adjust the interpolation weight for each pair correspondingly.
A Data-Centric Framework for Composable NLP Workflows
Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.
