In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Models

arXiv:2503.01611 · cs.CL · Submitted 2025-03-03 · Read on arXiv

cs.CL

Submitted: 2025-03-03

Updated: 2026-09-15

Code: https://github.com/Re-Align/AlignTDS

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 31× in this paper · explained in 1 episode
• Instruction Tuning — 28× in this paper · explained in 2 episodes
• Consistency — 10× in this paper · explained in 3 episodes
• Distribution Shift — 10× in this paper · explained in 6 episodes
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• In-Context Learning — 8× in this paper · explained in 4 episodes
• Direct Preference Optimisation — 7× in this paper
• Core — 4× in this paper · explained in 2 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Machine Translation — 2× in this paper
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Mixed Martial Arts — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes

Sources

• The Llama 3 Herd of Models
• In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning
• Instruction Following without Instruction Tuning
• Scaling Laws for Neural Language Models
• EuroLLM: Multilingual Language Models for Europe
• Finetuned Language Models Are Zero-Shot Learners

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