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arxiv: 2606.09525 · v1 · pith:5DMGNJLVnew · submitted 2026-06-08 · 💻 cs.CL · cs.AI

Emergence of Context Characteristics Sensitivity in Large Language Models

Nadya Yuki Wangsajaya , Haeun Yu , Isabelle Augenstein This is my paper

Pith reviewed 2026-06-27 16:22 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords context characteristicsinstruction fine-tuningsupervised fine-tuningdirect preference optimizationlarge language modelscontext utilizationreinforcement learning
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The pith

Context sensitivity in large language models emerges and shifts during successive instruction fine-tuning stages.

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

This paper tracks how large language models develop preferences for certain context characteristics as they go through instruction fine-tuning. It shows that supervised fine-tuning increases the tendency to rely on longer, more similar, and fluent contexts, while later optimization stages can either build on or undo that tendency depending on the dataset. A reader would care because it means that how models use context is not static but is shaped by training choices, affecting their reliability when context is provided. The experiments use four models and three datasets to demonstrate these dynamics across stages.

Core claim

The central claim is that context usage is actively reshaped at each instruction fine-tuning stage. Supervised fine-tuning makes models more likely to use contexts that are easy to understand due to high length, context-query similarity, and fluency. The dynamics after SFT either reinforce or resolve these preferences depending on the training dataset. Thus, designing a balanced IFT dataset is important for robust context utilization in instruction-tuned models.

What carries the argument

The shifts in sensitivity to context characteristics (length, context-query similarity, fluency) measured across the stages of supervised fine-tuning, direct preference optimization, and reinforcement learning with verifiable rewards.

If this is right

  • After supervised fine-tuning, models will show increased use of contexts with higher length, similarity, and fluency.
  • Later stages of direct preference optimization or reinforcement learning can either strengthen or weaken these context preferences based on the data.
  • Robust context utilization requires careful design of datasets at every instruction fine-tuning stage.
  • Models' behavior on tasks requiring context can vary systematically with the sequence of fine-tuning applied.

Where Pith is reading between the lines

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

  • Training sequences could be optimized to produce models that use context more uniformly regardless of its characteristics.
  • These shifts might affect model performance on real-world tasks where context quality varies.
  • If the measurement method holds, it opens the possibility of intervening at specific stages to correct undesired context biases.

Load-bearing premise

The metrics of context length, context-query similarity, and fluency, together with how sensitivity is quantified at each stage, correctly indicate actual reliance on the provided context instead of parametric knowledge.

What would settle it

An experiment showing that sensitivity patterns remain unchanged even when using a deliberately balanced IFT dataset across all stages would indicate the claim is incorrect.

Figures

Figures reproduced from arXiv: 2606.09525 by Haeun Yu, Isabelle Augenstein, Nadya Yuki Wangsajaya.

Figure 1
Figure 1. Figure 1: Experimental setup. From each dataset, three families of context characteristics (similarity, fluency, and overlap) are extracted. Accuracy is then calculated based on the model’s generated output. Sensitivity is measured by calculating the AUROC score (Section 4) between each characteristic and accuracy. may make models susceptible to injected misinfor￾mation (Pan et al., 2023). Understanding what a model… view at source ↗
Figure 2
Figure 2. Figure 2: Step-wise analysis across three datasets of Llama-3.2-1B (Section 2.2). Here, we show one metric of each characteristic family; other metrics are in Appendix D. During SFT (top), the model develops sensitivities towards easy-to-understand context, while DPO (bottom) resolves the learned sensitivities. Evaluation For each combination of model, dataset, and characteristic, we compute the AU￾ROC between the c… view at source ↗
Figure 3
Figure 3. Figure 3: Stage-wise analysis across four models on the Context-Reliance dataset. SFT induces the same sensitivity in all models, while DPO’s effect depends on characteristic differences in its training data. Evaluation results on ConflictQA and DRUID dataset are in Appendix F. Llama-3.2-1B Llama-3.1-8B Characteristic chosen rejected ∆ chosen rejected ∆ Context-query Jaccard similarity 0.158 0.150 0.008 0.192 0.161 … view at source ↗
Figure 4
Figure 4. Figure 4: Context-based accuracy of Llama-3.2-1B per step, throughout SFT (top) and DPO (bottom). Context-Reliance, the accuracy throughout SFT and DPO training hovers around 0.8 to 0.9. For DRUID, accuracy is lower at around 0.5, but it is still higher than random chance (0.33; three-way classification). This shows that we can use Llama￾3.2.1B as a proxy to study changes in context usage during SFT and DPO. C Confi… view at source ↗
Figure 5
Figure 5. Figure 5: Step-wise analysis between the remaining other context characteristic with accuracy including the unreliable context characteristic family. Llama-3.2-1B is used (Section 2.2). acteristic is normally distributed. H LLM Judge For ConflictQA and Context-Reliance dataset, if the model’s answer contains both (or neither of) context-based and parametric-based ground truths, we fall back to LLM judge to determine… view at source ↗
Figure 6
Figure 6. Figure 6: Stage-wise analysis across four models on two extra datasets: ConflictQA (top) and DRUID (bottom). 9 [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Prompts used for model output generation. [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prompt used for LLM judge as a tie-breaker. [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
read the original abstract

During instruction fine-tuning (IFT), large language models (LLMs) learn to follow instructions by using the provided context to answer a query. While prior work has studied how context characteristics correlate with context usage by the LLM, this analysis has been limited to inference time, leaving open how these relationships are acquired in the first place. Here, we measure how models' sensitivity to such characteristics shifts across successive IFT stages: supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning with verifiable rewards (RLVR). Experiments across four models and three datasets show that SFT makes models more likely to use contexts that are easy to understand, such as containing high length, context-query similarity, and fluency. Post-SFT dynamics may either reinforce or resolve these preferences depending on the training dataset. Our findings reveal that context usage is actively reshaped at each IFT stage, and designing a balanced IFT dataset is important in ensuring robust context utilization of instruction-tuned models.

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.

Referee Report

2 major / 2 minor

Summary. The paper claims that LLMs' sensitivity to context characteristics (length, context-query similarity, fluency) shifts across IFT stages (SFT, DPO, RLVR). Experiments on four models and three datasets indicate SFT increases use of easier contexts (high length/similarity/fluency), while later stages reinforce or resolve these preferences depending on the dataset; the central claim is that context usage is actively reshaped at each stage and balanced IFT datasets are needed for robust utilization.

Significance. If the measured shifts are reliable, the work offers observational evidence that IFT stages actively influence context utilization patterns, with implications for dataset design in instruction tuning. The multi-model, multi-dataset scope is a strength for generality, though the purely correlational nature on proxy metrics limits causal claims about actual context usage versus parametric knowledge.

major comments (2)
  1. [Abstract] Abstract: the reported shifts across stages are presented without error bars, statistical tests, exclusion criteria, or raw counts, which undermines assessment of whether the observed changes in sensitivity are statistically reliable or practically meaningful.
  2. [Results] Results/Methods: the sensitivity metrics (length, similarity, fluency) are used as proxies for context usage, but without explicit validation (e.g., controlled ablations showing these characteristics causally affect answer accuracy when context is required), the interpretation that IFT 'reshapes' usage rather than other behaviors remains tentative.
minor comments (2)
  1. [Methods] Provide the exact formulas or procedures used to compute context-query similarity and fluency scores.
  2. [Experimental Setup] Clarify the three datasets and four models by name and size in the experimental setup for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on statistical presentation and metric validation. We address each major comment below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the reported shifts across stages are presented without error bars, statistical tests, exclusion criteria, or raw counts, which undermines assessment of whether the observed changes in sensitivity are statistically reliable or practically meaningful.

    Authors: We agree that the absence of error bars, statistical tests, and raw counts limits evaluation of reliability. In the revised version we will: (i) add error bars (standard error across seeds or bootstrap) to all sensitivity plots, (ii) report paired statistical tests (e.g., Wilcoxon signed-rank) on the per-stage differences, (iii) include raw counts of contexts retained after filtering, and (iv) detail exclusion criteria in the Methods section. These additions will be reflected in both the abstract and results. revision: yes

  2. Referee: [Results] Results/Methods: the sensitivity metrics (length, similarity, fluency) are used as proxies for context usage, but without explicit validation (e.g., controlled ablations showing these characteristics causally affect answer accuracy when context is required), the interpretation that IFT 'reshapes' usage rather than other behaviors remains tentative.

    Authors: The study is explicitly observational and reports correlations between IFT stage and sensitivity to established proxy metrics drawn from prior context-usage literature. We do not claim direct causality. We will revise the discussion and limitations sections to state more explicitly that the metrics are proxies, that alternative behaviors cannot be ruled out without controlled ablations, and that the multi-model, multi-dataset consistency provides only correlational support. Performing new causal ablations is outside the scope of a revision; therefore we treat this as a clarification rather than a full experimental addition. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper reports purely observational experimental results on shifts in model sensitivity to context characteristics (length, similarity, fluency) across IFT stages (SFT, DPO, RLVR). No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or described methodology. All claims rest on direct measurements from four models and three datasets rather than any self-referential construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an empirical measurement study and therefore rests on standard statistical and experimental assumptions rather than new mathematical axioms or invented entities.

axioms (1)
  • standard math Standard statistical significance testing is sufficient to establish shifts across training stages
    Invoked implicitly when reporting that models become more likely to use certain contexts after SFT.

pith-pipeline@v0.9.1-grok · 5701 in / 1222 out tokens · 22824 ms · 2026-06-27T16:22:08.629456+00:00 · methodology

discussion (0)

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