Cover Image

English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dhaliwal, Mehak, Chaurasia, Shashwat, Qin, Yao, Hong, Dezhi, Butler, Thomas
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

NutriBench: A Dataset for Evaluating Large Language Models on Nutrition Estimation from Meal Descriptions
by: Hua, Andong, et al.
Published: (2024)

DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American English
by: Oh, Jio, et al.
Published: (2026)

Training on the Benchmark Is Not All You Need
by: Ni, Shiwen, et al.
Published: (2024)

Agents Are All You Need for LLM Unlearning
by: Sanyal, Debdeep, et al.
Published: (2025)

Not All Preferences are What You Need for Post-Training: Selective Alignment Strategy for Preference Optimization
by: Dong, Zhijin
Published: (2025)

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

Contrast Is All You Need
by: Kilic, Burak, et al.
Published: (2023)

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

A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism
by: Thompson, Brian, et al.
Published: (2024)

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
by: Lu, Xiaoding, et al.
Published: (2024)

Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication
by: Ma, Weicheng, et al.
Published: (2025)

The Roles of English in Evaluating Multilingual Language Models
by: Poelman, Wessel, et al.
Published: (2024)

Rho-1: Not All Tokens Are What You Need
by: Lin, Zhenghao, et al.
Published: (2024)

Not All Documents Are What You Need for Extracting Instruction Tuning Data
by: Zhang, Chi, et al.
Published: (2025)

Multilingual VLM Training: Adapting an English-Trained VLM to French
by: Lahmi, Jules, et al.
Published: (2025)

Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs
by: Wu, Zongqian, et al.
Published: (2025)

EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training
by: Dorkin, Aleksei, et al.
Published: (2026)

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

Self-Verification is All You Need To Pass The Japanese Bar Examination
by: Shin, Andrew
Published: (2026)

Sparsity May Be All You Need: Sparse Random Parameter Adaptation
by: Rios, Jesus, et al.
Published: (2025)

Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need
by: Bai, Yuqi, et al.
Published: (2025)

Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning
by: Lu, Jinghui, et al.
Published: (2025)

Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation
by: Chu, Yun-Wei, et al.
Published: (2024)

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
by: Bai, Yuelin, et al.
Published: (2024)

Rethinking Data Selection at Scale: Random Selection is Almost All You Need
by: Xia, Tingyu, et al.
Published: (2024)

Evaluation is All You Need: Strategic Overclaiming of LLM Reasoning Capabilities Through Evaluation Design
by: Sun, Lin, et al.
Published: (2025)

Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLM
by: Chimoto, Everlyn Asiko, et al.
Published: (2026)

Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?
by: Santosh, T. Y. S. S, et al.
Published: (2024)

Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning
by: Som, Anirudh, et al.
Published: (2023)

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

Attention Is All You Need for KV Cache in Diffusion LLMs
by: Nguyen-Tri, Quan, et al.
Published: (2025)

Pipeline Parallelism is All You Need for Optimized Early-Exit Based Self-Speculative Decoding
by: Li, Ruanjun, et al.
Published: (2025)

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

Pretraining on the Test Set Is No Longer All You Need: A Debate-Driven Approach to QA Benchmarks
by: Cao, Linbo, et al.
Published: (2025)

Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
by: Goldman, Omer, et al.
Published: (2024)

Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
by: Chen, Lingjiao, et al.
Published: (2024)

SecEncoder: Logs are All You Need in Security
by: Bulut, Muhammed Fatih, et al.
Published: (2024)

SurfaceLogicKV: Surface and Logic Attention Behaviors are All You Need for Robust KV Cache Compression
by: Li, Mengjie, et al.
Published: (2025)

Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
by: Bsharat, Sondos Mahmoud, et al.
Published: (2023)

s3: You Don't Need That Much Data to Train a Search Agent via RL
by: Jiang, Pengcheng, et al.
Published: (2025)