Imagen de Portada

You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Mąka, Paweł, Semerci, Yusuf Can, Scholtes, Jan, Spanakis, Gerasimos
Formato:	Preprint
Publicado:	2025
Materias:	Computation and Language Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models
por: Mąka, Paweł, et al.
Publicado: (2024)

Sequence Shortening for Context-Aware Machine Translation
por: Mąka, Paweł, et al.
Publicado: (2024)

Cross-Modal Robustness Transfer (CMRT): Training Robust Speech Translation Models Using Adversarial Text
por: Issam, Abderrahmane, et al.
Publicado: (2026)

Fixed and Adaptive Simultaneous Machine Translation Strategies Using Adapters
por: Issam, Abderrahmane, et al.
Publicado: (2024)

DTW-Align: Bridging the Modality Gap in End-to-End Speech Translation with Dynamic Time Warping Alignment
por: Issam, Abderrahmane, et al.
Publicado: (2025)

Language Models as Artificial Learners: Investigating Crosslinguistic Influence
por: Issam, Abderrahmane, et al.
Publicado: (2026)

A Representation Level Analysis of NMT Model Robustness to Grammatical Errors
por: Issam, Abderrahmane, et al.
Publicado: (2025)

Navigating WebAI: Training Agents to Complete Web Tasks with Large Language Models and Reinforcement Learning
por: Thil, Lucas-Andreï, et al.
Publicado: (2024)

More Compute Is What You Need
por: Guo, Zhen
Publicado: (2024)

DIDS: Domain Impact-aware Data Sampling for Large Language Model Training
por: Shi, Weijie, et al.
Publicado: (2025)

Did You Forget What I Asked? Prospective Memory Failures in Large Language Models
por: Mittal, Avni
Publicado: (2026)

What Would You Ask When You First Saw $a^2+b^2=c^2$? Evaluating LLM on Curiosity-Driven Questioning
por: Javaji, Shashidhar Reddy, et al.
Publicado: (2024)

Automated Multi-Language to English Machine Translation Using Generative Pre-Trained Transformers
por: Pelofske, Elijah, et al.
Publicado: (2024)

Forget What You Know about LLMs Evaluations -- LLMs are Like a Chameleon
por: Cohen-Inger, Nurit, et al.
Publicado: (2025)

Synthetic Data RL: Task Definition Is All You Need
por: Guo, Yiduo, et al.
Publicado: (2025)

Regurgitative Training: The Value of Real Data in Training Large Language Models
por: Zhang, Jinghui, et al.
Publicado: (2024)

You Only Train Once: Differentiable Subset Selection for Omics Data
por: Chopard, Daphné, et al.
Publicado: (2025)

Planning and Editing What You Retrieve for Enhanced Tool Learning
por: Huang, Tenghao, et al.
Publicado: (2024)

Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training
por: Guo, Qingyan, et al.
Publicado: (2024)

ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning
por: Zeng, Xingshan, et al.
Publicado: (2025)

APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
por: Guan, Ziyi, et al.
Publicado: (2024)

Many-to-English Machine Translation Tools, Data, and Pretrained Models
por: Gowda, Thamme, et al.
Publicado: (2021)

You Can Generate It Again: Data-to-Text Generation with Verification and Correction Prompting
por: Ren, Xuan, et al.
Publicado: (2023)

More Agents Is All You Need
por: Li, Junyou, et al.
Publicado: (2024)

Reasoning with Sampling: Your Base Model is Smarter Than You Think
por: Karan, Aayush, et al.
Publicado: (2025)

Should You Use Your Large Language Model to Explore or Exploit?
por: Harris, Keegan, et al.
Publicado: (2025)

Fast Training Dataset Attribution via In-Context Learning
por: Fotouhi, Milad, et al.
Publicado: (2024)

Data Efficacy for Language Model Training
por: Dai, Yalun, et al.
Publicado: (2025)

Tensor Product Attention Is All You Need
por: Zhang, Yifan, et al.
Publicado: (2025)

LoRA Is Slower Than You Think
por: Ko, Seokmin
Publicado: (2025)

Attention Smoothing Is All You Need For Unlearning
por: Zade, Saleh Zare, et al.
Publicado: (2026)

InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory
por: Xiao, Chaojun, et al.
Publicado: (2024)

Guidance is All You Need: Temperature-Guided Reasoning in Large Language Models
por: Gomaa, Eyad, et al.
Publicado: (2024)

SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
por: Kulkarni, Atharva, et al.
Publicado: (2024)

Optimisation Is Not What You Need
por: Ibias, Alfredo
Publicado: (2025)

Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training
por: Kesgin, H. Toprak, et al.
Publicado: (2024)

Context is All You Need
por: Delanois, Jean Erik, et al.
Publicado: (2026)

Training and Evaluating Language Models with Template-based Data Generation
por: Zhang, Yifan
Publicado: (2024)

Does Training on Synthetic Data Make Models Less Robust?
por: Zhang, Lingze, et al.
Publicado: (2025)

Is Child-Directed Speech Effective Training Data for Language Models?
por: Feng, Steven Y., et al.
Publicado: (2024)