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

Enhao Huang; Hongbin Zhou; Kui Ren; Lan Tao; Wenze Lin; Yiming Li; Yuchen Yang; Zhan Qin; Zhixuan Chu

arxiv: 2602.14536 · v3 · submitted 2026-02-16 · 💻 cs.CL · cs.AI

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

Yuchen Yang , Wenze Lin , Enhao Huang , Zhixuan Chu , Hongbin Zhou , Lan Tao , Yiming Li , Zhan Qin

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Kui Ren

This is my paper

Pith reviewed 2026-05-15 21:58 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords token-level noise filteringLLM fine-tuninggradient maskingdataset optimizationexplainable trainingreasoning importanceknowledge noveltytask relevance

0 comments

The pith

XTF filters noisy tokens during LLM fine-tuning by scoring three token attributes and masking their gradients.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models optimize at the token level during fine-tuning, yet most training datasets are prepared at the sentence level, introducing token-level noise that hurts final results. This paper presents XTF, a framework that decomposes each token's contribution into three explicit attributes: reasoning importance, knowledge novelty, and task relevance. Scoring methods evaluate these attributes, and the gradients of tokens identified as noisy are masked to prevent negative influence. Experiments on math, code, and medicine tasks across seven mainstream LLMs show accuracy gains of up to 13.7 percent over standard fine-tuning. A sympathetic reader would care because the method improves existing datasets without requiring new data collection or architecture changes.

Core claim

XTF decomposes the complex contributions of individual tokens to the fine-tuning process into three distinct attributes—reasoning importance, knowledge novelty, and task relevance—which are assessed with scoring methods; gradients of tokens judged noisy are then masked to optimize the performance of fine-tuned LLMs on downstream tasks.

What carries the argument

The XTF framework, which decomposes token contributions into three scored attributes and applies targeted gradient masking to remove noise.

If this is right

Performance on math, code, and medicine tasks improves by measurable margins over regular fine-tuning.
The training process becomes more explainable through explicit attribute decomposition.
Gradient masking based on token scores reduces negative effects from sentence-level dataset design.
The approach generalizes across seven different LLMs without model-specific redesign.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same attribute decomposition could be tested on pre-training corpora to reduce noise earlier in the pipeline.
Integrating XTF with active learning or data augmentation might amplify gains by creating cleaner training signals.
The scoring methods might reveal systematic patterns of noise that point to better dataset construction practices.

Load-bearing premise

The three scoring methods can reliably separate noisy tokens from useful ones without discarding information that is critical for the downstream task.

What would settle it

Apply XTF to a controlled dataset containing known critical tokens that are necessary for task success and check whether performance falls rather than rises.

read the original abstract

Large Language Models (LLMs) have seen remarkable advancements, achieving state-of-the-art results in diverse applications. Fine-tuning, an important step for adapting LLMs to specific downstream tasks, typically involves further training on corresponding datasets. However, a fundamental discrepancy exists between current fine-tuning datasets and the token-level optimization mechanism of LLMs: most datasets are designed at the sentence-level, which introduces token-level noise, causing negative influence to final performance. In this paper, we propose XTF, an explainable token-level noise filtering framework. XTF decomposes the complex and subtle contributions of token-level data to the fine-tuning process into three distinct and explicit attributes (reasoning importance, knowledge novelty, and task relevance), which can be assessed using scoring methods, and then masks the gradients of selected noisy tokens accordingly to optimize the performance of fine-tuned LLMs. We conduct extensive experiments on three representative downstream tasks (math, code and medicine) across 7 mainstream LLMs. The results demonstrate that XTF can significantly improve downstream performance by up to 13.7% compared to regular fine-tuning. Our work highlights the importance of token-level dataset optimization, and demonstrates the potential of strategies based on attribute decomposition for explaining complex training mechanisms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

XTF introduces a three-attribute token decomposition for noise filtering that goes beyond sentence-level methods, but the scoring details are too thin to confirm the gains aren't just from smaller datasets.

read the letter

The main thing to know is that this paper puts forward XTF, which splits each token's role in fine-tuning into reasoning importance, knowledge novelty, and task relevance, then masks gradients on the tokens it flags as noisy. That explicit breakdown is a clear step past the sentence-level filters cited in the abstract, and the experiments run it on math, code, and medicine tasks across seven LLMs with reported gains up to 13.7 percent over plain fine-tuning. The coverage across models and domains is useful and gives the claim some weight. The soft spot is exactly what the stress-test note flags: the abstract gives almost no concrete description of how the three scores are actually calculated or validated. If those scores turn out to be derived from the same loss surface the model is optimizing, or if they simply correlate with token rarity, the improvement could be an artifact of training on less data rather than targeted denoising. There are no ablations shown against random masking or controls for total token count, so the causal link stays unproven. This is the sort of paper that would interest people working on data-efficient adaptation for technical domains. The core idea has enough structure and the experimental scope is broad enough that it deserves referee time, provided the reviewers can get the scoring implementations and some validation experiments. I would send it to review rather than desk reject.

Referee Report

3 major / 2 minor

Summary. The paper proposes XTF, an explainable token-level noise filtering framework for LLM fine-tuning. It decomposes token contributions into three attributes (reasoning importance, knowledge novelty, task relevance) scored by unspecified methods, then masks gradients of selected noisy tokens. Experiments across math, code, and medicine tasks on seven LLMs report up to 13.7% gains over standard fine-tuning.

Significance. If the scoring functions can be shown to separate noise from task-critical tokens without circular dependence on the fine-tuning loss, the work would offer a concrete, interpretable method for token-level dataset optimization that directly addresses the mismatch between sentence-level data and token-level training. The multi-LLM, multi-task experimental scope is a strength.

major comments (3)

[Abstract and §3] Abstract and §3 (scoring methods): the three attribute scores are introduced without explicit formulations, equations, or pseudocode. This prevents assessment of whether the scores are independent of the fine-tuning loss or whether they risk discarding rare but task-critical tokens (e.g., domain-specific medical terms).
[§4] §4 (experiments): no controls are described for total training steps, effective data volume after masking, or learning-rate schedules when comparing XTF to regular fine-tuning. The reported 13.7% gain could therefore be an artifact of reduced data volume rather than targeted denoising.
[§4 and §5] §4 and §5 (validation): the manuscript contains no oracle comparisons, controlled noise-injection experiments, or ablation on the masking threshold that would confirm the scores track actual noise rather than dataset shrinkage. This is load-bearing for the central claim.

minor comments (2)

[§3] Add explicit equations for each of the three scoring functions and state whether any parameters are learned or fixed.
[§3] Clarify the exact masking procedure (per-token gradient scaling factor, threshold selection) and report the fraction of tokens masked per dataset.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to incorporate clarifications and additional experiments where needed.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (scoring methods): the three attribute scores are introduced without explicit formulations, equations, or pseudocode. This prevents assessment of whether the scores are independent of the fine-tuning loss or whether they risk discarding rare but task-critical tokens (e.g., domain-specific medical terms).

Authors: We agree the scoring methods must be formalized. The manuscript describes the three attributes conceptually but omits the exact computation procedures. In the revised version we will insert explicit equations and pseudocode in §3 for reasoning importance, knowledge novelty, and task relevance, along with a discussion of their independence from the fine-tuning loss and an analysis confirming that domain-specific terms are retained at high rates. revision: yes
Referee: [§4] §4 (experiments): no controls are described for total training steps, effective data volume after masking, or learning-rate schedules when comparing XTF to regular fine-tuning. The reported 13.7% gain could therefore be an artifact of reduced data volume rather than targeted denoising.

Authors: We acknowledge the need for explicit controls. All experiments used identical training steps, batch sizes, and learning-rate schedules; masking only suppresses gradients on selected tokens without altering the number of epochs or optimizer steps. In the revision we will add a dedicated paragraph and table reporting average masked-token fractions per task and confirming that effective compute remains matched across conditions. revision: yes
Referee: [§4 and §5] §4 and §5 (validation): the manuscript contains no oracle comparisons, controlled noise-injection experiments, or ablation on the masking threshold that would confirm the scores track actual noise rather than dataset shrinkage. This is load-bearing for the central claim.

Authors: We agree that direct validation experiments are required to substantiate the claim. While cross-task and cross-model gains provide supporting evidence, we will add in the revision: an ablation on the masking threshold, a controlled synthetic-noise injection study on a held-out subset, and an oracle comparison that masks only known noisy tokens. These results will be reported in §4 and §5. revision: yes

Circularity Check

0 steps flagged

No circularity detected in XTF derivation chain

full rationale

The paper introduces XTF as a framework that decomposes token contributions into three explicit attributes (reasoning importance, knowledge novelty, task relevance) assessed by scoring methods followed by gradient masking. No equations, self-citations, or fitted-parameter renamings appear in the abstract or described text that would reduce any claimed prediction or result to the inputs by construction. The reported performance improvements (up to 13.7%) are presented as outcomes of experiments on math, code, and medicine tasks across multiple LLMs, which constitute independent empirical validation rather than tautological re-derivation. The scoring methods are framed as independent assessments, with no indication that they are derived from the same loss they aim to optimize or that uniqueness is imported from prior self-work. This is a standard proposal-plus-experiment structure with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The framework rests on the assumption that token utility can be factored into three independent, scorable attributes and that masking gradients on low-scoring tokens improves learning without side effects.

free parameters (1)

masking threshold
Value used to decide which tokens receive zero gradient; not specified in abstract but required for the method.

axioms (1)

domain assumption Token contributions to fine-tuning can be decomposed into reasoning importance, knowledge novelty, and task relevance
Central premise stated in the abstract.

pith-pipeline@v0.9.0 · 5537 in / 1169 out tokens · 16949 ms · 2026-05-15T21:58:40.922627+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

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IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

XTF decomposes ... into three distinct ... attributes (reasoning importance, knowledge novelty, and task relevance), which can be assessed using scoring methods, and then masks the gradients of selected noisy tokens

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