arxiv: 2604.25132 · v1 · submitted 2026-04-28 · 💻 cs.CL

Recognition: unknown

What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective

Guangzeng Han , Xiaolei Huang

Authors on Pith no claims yet

Pith reviewed 2026-05-07 16:33 UTC · model grok-4.3

classification 💻 cs.CL

keywords instruction tuningdata selectionin-context learninginfluencedata qualityfine-tuninginstruction following

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The pith

Instruction data selected by how much each example helps similar peers in context produces better-tuned models than baselines under tight data limits.

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

The paper introduces a selection method that scores each candidate instruction example by its weighted in-context influence on semantically related peers, specifically how well it reduces their instruction-following difficulty. Experiments on multiple models and benchmarks show this selection beats existing approaches when only a small subset of data is used for tuning. The work also finds that harder samples tend to exert less positive influence in this in-context setting. By framing data quality through an in-context lens, the approach offers a practical way to prune redundant or low-value examples before full fine-tuning.

Core claim

Effective instruction-tuning data consists of examples that carry high weighted in-context influence, meaning they measurably ease instruction-following for other semantically related examples when used as in-context demonstrations; selecting on this basis yields stronger downstream performance than difficulty-based or random selection, and sample difficulty correlates negatively with in-context influence.

What carries the argument

Weighted in-context influence (wICI), a score that measures an example's ability to reduce instruction-following difficulty for semantically related held-out peers when the example is provided as an in-context demonstration.

If this is right

Tuning with fewer but higher-wICI examples reaches higher performance than using larger random or difficulty-selected sets.
Difficulty metrics alone are insufficient for data selection because they move opposite to in-context influence.
Data selection can be performed by computing influence scores on a modest set of peers without running full fine-tuning on every candidate.
The in-context view supplies an operational definition of data quality that directly links to the mechanism used during later inference.

Where Pith is reading between the lines

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

The same scoring idea could be applied to other fine-tuning regimes where in-context effects dominate, such as few-shot adaptation of larger models.
Curators might combine wICI with cheap pre-filters to reduce the pool before any model-based scoring.
If the negative difficulty-influence correlation holds on new domains, it would suggest avoiding the hardest examples when building compact instruction sets.

Load-bearing premise

The in-context influence score measured on held-out peers accurately predicts which examples will improve a model's instruction-following ability after full fine-tuning on the selected data.

What would settle it

Fine-tune a model on the highest-wICI examples and observe that its benchmark scores are no higher than those from a random or difficulty-ranked subset of the same size.

Figures

Figures reproduced from arXiv: 2604.25132 by Guangzeng Han, Xiaolei Huang.

**Figure 1.** Figure 1: Overview of our framework. Semantic Retrieval. In-context influence can only be measured meaningfully when the probes share a coherent topical space with the instruction; otherwise, their responses may reflect task mismatch rather than demonstrational assistance. For each instruction xi , we retrieve its N nearest neighbors in the embedding space under the Euclidean distance: N N i = arg topN j̸=i view at source ↗

**Figure 2.** Figure 2: Visualization of the verb–noun structures in instructions selected by the three data-selection strategies on view at source ↗

**Figure 3.** Figure 3: Winning-score curves of LLAMA3.1-8B trained on Alpaca-GPT4. achieving superior sample efficiency. 6.4 Domain Generalization Analysis To examine whether our data selection method generalizes beyond the Alpaca-GPT4 and WizardLM settings, we additionally conduct experiments in a medical domain using the MedQuAD (Ben Abacha and Demner-Fushman, 2019) dataset. Specifically, we select 30% of the training data wi… view at source ↗

**Figure 4.** Figure 4: Prompt template used to elicit a discrete complexity level (1–6) from the DEITA complexity scorer. At view at source ↗

**Figure 5.** Figure 5: Prompt template used for pairwise evaluation with GPT-4.1-mini as judge. view at source ↗

**Figure 6.** Figure 6: Visualization of the verb–noun structures in instructions selected by the three data-selection strategies on view at source ↗

read the original abstract

Instruction-tuning datasets often contain substantial redundancy and low-quality samples, necessitating effective data selection methods. We propose an instruction data selection framework based on weighted in-context influence (wICI), which measures how effectively each candidate example reduces instruction-following difficulty for semantically related peers. Through systematic experiments, we address three key questions: what constitutes effective instruction tuning data from an in-context perspective, whether sample difficulty correlates with in-context influence, and how in-context influence translates to instruction tuning effectiveness. Experiments across multiple models and benchmarks demonstrate that our method consistently outperforms existing baselines under constrained data budgets, while empirically showing that sample difficulty negatively correlates with in-context influence.

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

wICI gives a new angle on data selection via in-context influence and shows experimental gains over baselines, but the ICL scoring to actual SFT improvement link is still unproven.

read the letter

The main things to know are that the paper introduces a weighted in-context influence score to rank instruction examples by how much they help similar held-out peers, and their tests across models and benchmarks show this selection beats existing baselines when data budgets are tight. They also report a negative correlation between sample difficulty and this influence measure. Both findings come from addressing three explicit questions about effective data from an in-context view, the difficulty link, and downstream effectiveness. The experiments are systematic enough to give the claims some weight, and the outperformance under constraints is a practical plus for anyone doing efficient tuning. The framework itself is a reasonable extension of ICL ideas into curation rather than a rehash of prior influence or difficulty methods. The soft spot is the transfer step. Scoring happens through prompt-based ICL on frozen models and held-out peers, while the payoff is measured after gradient-based fine-tuning, so the rankings could easily diverge. The abstract gives no details on how the peer set is chosen, how semantic similarity is computed for weighting, or whether error bars were used, which leaves the robustness unclear. The stress-test point about ICL and SFT operating in different regimes lands here; nothing shown so far guarantees the held-out distribution matches what the model sees post-tuning. This work is aimed at NLP people focused on data-efficient instruction tuning. A reader already thinking about selection methods would pick up the framework and the correlation result as useful starting points. It has enough substance and consistent patterns to warrant peer review, though the referees will likely ask for more direct evidence that the ICL scores predict the SFT gains and for fuller experimental controls.

Referee Report

2 major / 2 minor

Summary. The paper claims that a weighted in-context influence (wICI) measure can effectively select high-quality instruction-tuning data by quantifying how each example reduces instruction-following difficulty for semantically similar peers in an in-context setting. Systematic experiments across models and benchmarks show that this selection outperforms existing baselines under data budgets and that sample difficulty negatively correlates with in-context influence.

Significance. If the results hold, this work provides a computationally efficient method for curating instruction data without requiring full fine-tuning for evaluation, which is valuable given the redundancy in instruction datasets. The consistent outperformance and the empirical correlation offer both practical utility and theoretical insight into data quality from an ICL perspective. The multi-model validation is a strength.

major comments (2)

The reported outperformance lacks error bars, details on random seeds, and exact selection procedures for the held-out peers, which are necessary to verify the robustness of the central claim that wICI-based selection is superior.
The transferability of wICI scores from ICL on held-out peers to actual SFT performance gains is assumed but not directly tested; since ICL uses frozen weights and prompt-based inference while SFT involves gradient updates, an ablation comparing ICL rankings to SFT outcomes on the same subsets would strengthen the claim.

minor comments (2)

The abstract could more explicitly state the three key questions addressed in the experiments.
Clarify the formula for the weighted in-context influence score, perhaps with a dedicated equation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of our work's significance. We address each major comment below with specific plans for revision where appropriate, focusing on improving reproducibility and strengthening the link between our metric and SFT outcomes.

read point-by-point responses

Referee: The reported outperformance lacks error bars, details on random seeds, and exact selection procedures for the held-out peers, which are necessary to verify the robustness of the central claim that wICI-based selection is superior.

Authors: We agree that these details are essential for verifying robustness. In the revised manuscript, we will add error bars computed across multiple runs (at least three) with different random seeds for all main results tables and figures. We will explicitly state the random seeds used and provide a precise description of the held-out peer selection procedure, including the embedding model, similarity threshold, and sampling method for peers. This will directly address the concern and improve reproducibility. revision: yes
Referee: The transferability of wICI scores from ICL on held-out peers to actual SFT performance gains is assumed but not directly tested; since ICL uses frozen weights and prompt-based inference while SFT involves gradient updates, an ablation comparing ICL rankings to SFT outcomes on the same subsets would strengthen the claim.

Authors: We acknowledge the value of directly testing this transferability, as ICL and SFT differ in their mechanisms. Our current experiments already show that wICI-selected data improves SFT performance over baselines, but we agree a targeted ablation would strengthen the argument. In the revision, we will add a discussion of the ICL-to-SFT assumption and include a limited ablation study (on a subset of models and data budgets) that compares SFT performance using subsets ranked by wICI versus other methods. We will also elaborate on why ICL provides an efficient proxy for data quality without requiring full fine-tuning for every candidate. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical validation is independent of definition

full rationale

The paper defines wICI as an empirical measure of in-context influence on held-out peers and then validates selection via downstream SFT benchmarks. No equations, self-citations, or derivations reduce the claimed outperformance or difficulty-influence correlation to a fitted parameter or input by construction. The central result rests on experimental comparison rather than tautology, satisfying the criteria for a self-contained non-circular analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities beyond the wICI metric itself; full paper would be needed to audit these.

invented entities (1)

weighted in-context influence (wICI) no independent evidence
purpose: Quantify how much each candidate example reduces instruction-following difficulty for semantically related peers
Core new metric introduced to drive data selection

pith-pipeline@v0.9.0 · 5397 in / 1051 out tokens · 44292 ms · 2026-05-07T16:33:07.952982+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

11 extracted references · 8 canonical work pages · 7 internal anchors

[1]

InFirst Confer- ence on Language Modeling

Instruction mining: Instruction data selection for tuning large language models. InFirst Confer- ence on Language Modeling. Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srini- vasan, Tianyi Zhou, Heng Huang, and Hongxia Jin
[2]

Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Alpagasus: Training a better alpaca with fewer data. InThe Twelfth International Conference on Learning Representations. Yicheng Chen, Yining Li, Kai Hu, Ma Zerun, HaochenYe HaochenYe, and Kai Chen. 2025. MIG: Automatic data selection for instruction tuning by maximizing information gain in semantic space. In Findings of the Association for Computational ...

work page internal anchor Pith review arXiv 2025
[3]

Training Verifiers to Solve Math Word Problems

Training verifiers to solve math word prob- lems.arXiv preprint arXiv:2110.14168. Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Shuming Ma, Zhifang Sui, and Furu Wei. 2023. Why can GPT learn in-context? language models secretly perform gradient descent as meta-optimizers. InFindings of the Association for Computational Linguistics: ACL 2023, pages 4005–4019, T...

work page internal anchor Pith review arXiv 2023
[4]

The Llama 3 Herd of Models

A survey on in-context learning. InProceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1107–1128, Miami, Florida, USA. Association for Computational Linguistics. Hanyu Duan, Yixuan Tang, Yi Yang, Ahmed Abbasi, and Kar Yan Tam. 2024. Exploring the relationship between in-context learning and instruction tuning. I...

work page internal anchor Pith review arXiv 2024
[5]

Guangzeng Han, Weisi Liu, and Xiaolei Huang

A framework for few-shot language model evaluation. Guangzeng Han, Weisi Liu, and Xiaolei Huang. 2025. Attributes as textual genes: Leveraging LLMs as ge- netic algorithm simulators for conditional synthetic data generation. InFindings of the Association for Computational Linguistics: EMNLP 2025, pages 19367–19389, Suzhou, China. Association for Com- puta...

2025
[6]

Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Measuring massive multitask language under- standing. InInternational Conference on Learning Representations. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2021. What disease does this patient have? a large-scale open domain question answering dataset from medical exams.Ap- plied Sciences, 11(14):6421. Nathan Lamber...

work page internal anchor Pith review arXiv 2021
[7]

Instruction Tuning with GPT-4

Call for rigor in reporting quality of instruction tuning data. InProceedings of the 63rd Annual Meet- ing of the Association for Computational Linguistics (Volume 2: Short Papers), pages 100–109, Vienna, Austria. Association for Computational Linguistics. Marius Mosbach, Tiago Pimentel, Shauli Ravfogel, Di- etrich Klakow, and Yanai Elazar. 2023. Few-shot...

work page internal anchor Pith review arXiv 2023
[8]

Proximal Policy Optimization Algorithms

Direct preference optimization: Your language model is secretly a reward model.Advances in neural information processing systems, 36:53728–53741. Hanshu Rao, Weisi Liu, Haohan Wang, I-Chan Huang, Zhe He, and Xiaolei Huang. 2026. A scoping review of synthetic data generation by language models in biomedical research and application: Data utility and qualit...

work page internal anchor Pith review arXiv 2026
[9]

In Forty-second International Conference on Machine Learning

NICE data selection for instruction tuning in LLMs with non-differentiable evaluation metric. In Forty-second International Conference on Machine Learning. Liang Wang, Nan Yang, and Furu Wei. 2024a. Learning to retrieve in-context examples for large language models. InProceedings of the 18th Conference of the European Chapter of the Association for Compu-...

work page arXiv 2023
[10]

Instruction-Following Evaluation for Large Language Models

Llamafactory: Unified efficient fine-tuning of 100+ language models. InProceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (Volume 3: System Demonstra- tions), Bangkok, Thailand. Association for Computa- tional Linguistics. Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Ef...

work page internal anchor Pith review arXiv
[11]

Fine-tuning is performed with DeepSpeed ZeRO-3 for memory optimization and bf16 mixed precision, using input sequences truncated to 2,048 tokens

to fully fine-tune Llama3.1-8B, and Mistral- 7B-v0.3. Fine-tuning is performed with DeepSpeed ZeRO-3 for memory optimization and bf16 mixed precision, using input sequences truncated to 2,048 tokens. Each model is trained for three epochs with the AdamW optimizer, an initial learning rate of 1×10 −5, a cosine-annealing learning rate schedule with linear w...

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