Explainable Token-level Noise Filtering for LLM Fine-tuning Datasets

arXiv:2602.14536 · cs.CL, cs.AI · Submitted 2026-02-16 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-02-16

Updated: 2026-09-08

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

Terminology

• Fine-Tuning — 98× in this paper · explained in 5 episodes
• Alignment — 23× in this paper · explained in 1 episode
• Accuracy — 22× in this paper · explained in 1 episode
• Reasoning Importance — 15× in this paper
• Task Relevance — 15× in this paper
• Knowledge Novelty — 14× in this paper
• Large Language Model — 14× in this paper
• Downstream Tasks — 12× in this paper · explained in 2 episodes
• Core — 9× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Ablation study — 8× in this paper · explained in 1 episode
• Distillation — 7× in this paper · explained in 2 episodes
• Data Augmentation — 6× in this paper · explained in 7 episodes
• Perplexity — 5× in this paper · explained in 3 episodes
• Explainability — 4× in this paper · explained in 3 episodes
• Knowledge Distillation — 4× in this paper · explained in 11 episodes
• Selection Bias — 4× in this paper · explained in 4 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Fisher Information — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Parameter-Efficient Fine-Tuning — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adaptive Mechanism — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Token-Level Supervised Language Model — 1× in this paper
• Transformer Models — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• Qwen Technical Report
• Evaluating Large Language Models Trained on Code
• Training Verifiers to Solve Math Word Problems
• The Llama 3 Herd of Models
• A Toffoli Gate Decomposition via Echoed Cross-Resonance Gates
• Large Language Model based Multi-Agents: A Survey of Progress and Challenges
• Memory-efficient Transformers via Top-$k$ Attention
• Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
• Measuring Mathematical Problem Solving With the MATH Dataset
• Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations
• Mistral 7B
• GPT-4 Technical Report
• Rethinking Interpretability in the Era of Large Language Models
• Gemma 2: Improving Open Language Models at a Practical Size
• Llama 2: Open Foundation and Fine-Tuned Chat Models

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