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Understanding the Role of Prompt Template in Knowledge Distillation for Safety Alignment
Anjila Budathoki, Manish Dhakal, Benjamin M. Ampel, Yi Ding
TL;DR
The paper examines how chat and non-chat prompt templates affect the preservation of safety alignment during knowledge distillation, a question that remains underexplored relative to SFT. It compares the templates in benign-task distillation and finds that chat formatting is associated with greater safety degradation and representational change.
Problem
How prompt-template choice during knowledge distillation affects preservation of students’ pre-existing safety alignment remains empirically underexplored.
Source: §1 · p. 1Method
The study distills students on benign instruction-following data with chat or non-chat templates, evaluates with a standard chat template, and analyzes refusal representations and template mixing.
Source: §1 · p. 2Results
Chat-template KD yields higher ASR than non-chat KD and shifts refusal representations away from the base model while weakening harmful-versus-harmless prompt separation.
Source: §1 · p. 2Takeaways & Limitations
Prompt formatting during KD is a training-time factor associated with amplified safety degradation, while non-chat KD largely preserves refusal representations.
Source: §1 · p. 2, §6 · p. 9The study uses only benign instruction-following data, leaving template-driven degradation in structurally richer domains such as code, math, or reasoning untested.
Source: Limitations · p. 10
Abstract
from arXiv · showhide
Prior research has demonstrated that the choice of prompt template during Supervised Fine-Tuning (SFT) significantly impacts the robustness of safety alignment afterwards. However, the influence of template selection during Knowledge Distillation (KD) from teacher to student remains largely unexplored. Thus, we fill this gap by analyzing how different template configurations influence the pre-existing safety alignment of the student. We observe a significant degradation of safety alignment present in the aligned base instruct-tuned model. Specifically, we find that utilizing chat templates renders the model more compliant with harmful queries compared to a non-chat template. These findings are consistent across three models: LLaMA, Gemma and Qwen model families and are evaluated across multiple safety benchmarks. We further show that using a non-chat template during distillation better preserves the base student's internal representations, while chat template distillation induces a larger representational shift. Code: https://github.com/anjilab/role-of-prompt-template-in-kd
1 Introduction
The paper asks whether training-time prompt templates affect the safety alignment of students during knowledge distillation, a question distinct from prior SFT findings. It compares chat and non-chat distillation on benign instruction-following data and finds that chat formatting is associated with greater safety degradation.
1 Introduction
- Chat templates add conversational control tokens, whereas non-chat templates present the same content without them; their role in KD remains underexplored despite SFT evidence.The paper frames KD as distinct from SFT because students receive teacher-generated soft targets that may interact with formatting.Source: §1 · p. 1
- Benign-data knowledge distillation can erode pre-existing safety alignment in aligned student models.The paper examines this risk in the context of student models that are expected to refuse harmful or policy-violating requests.Source: §1 · pp. 1–2
- Chat-template KD consistently produces higher Attack Success Rate than non-chat KD under identical training data.All models are evaluated using the standard chat template at inference, so the comparison concerns training-time template choices.Source: §1 · p. 2
- Chat-template KD shifts refusal representations from the base model and weakens separation between harmful and harmless prompts, while non-chat largely preserves both.The paper tests whether the output-level safety gap reflects changes in internal refusal representations or only a surface-level effect.Source: §1 · p. 2
- As the share of chat-template samples rises, ASR generally increases while utility improves only modestly.The study varies the proportion of chat and non-chat samples to characterize how safety and utility scale with chat exposure.Source: §1 · p. 2
2 Related work
Related work covers knowledge distillation for compact language models, safety alignment, and prompt-template effects. It identifies the impact of chat versus non-chat templates during benign-task KD as an empirical gap.
Knowledge Distillation
- Prior KD work emphasizes task utility, while the effect of distillation on safety alignment remains understudied.KD transfers teacher knowledge to smaller students, including through soft targets and objectives developed for generative language models.Source: §2 · pp. 2–3
Safety Alignment in LLMs
- Alignment methods can embed refusal behavior, but vulnerabilities in open-access models permit jailbreaks, and benign-task KD may further degrade guardrails.The cited work leaves unresolved whether distilling previously aligned models on benign downstream tasks weakens their safety guardrails.Source: §2 · p. 3
Prompt Templates
- Prompt formatting affects model behavior and safety during fine-tuning, but its role in knowledge distillation remains underexplored.Prior findings include reduced context awareness and safety failures with some chat-template designs, while task-style fine-tuning and safety-oriented testing can better preserve safe behavior.Source: §2 · p. 3
3 Methodology
The methodology compares chat and non-chat prompt formatting during knowledge distillation while holding inference formatting constant. It combines supervised and teacher-distillation objectives and evaluates three model families on instruction-following utility and safety benchmarks.
3 Methodology
- The pipeline constructs instruction datasets with chat or non-chat formatting, then fine-tunes students using a balanced knowledge-distillation objective.The objective combines supervised learning with teacher-distribution transfer.Source: §3 · p. 3
3.1 Prompt Formatting
- Chat formatting uses each model family’s native control tokens, whereas non-chat formatting strips them; every model is evaluated with the standard chat template.This holds inference formatting constant across training conditions.Source: §3.1 · p. 3
3.2 Distillation Objective
- The student objective combines gold-reference cross-entropy with forward KL distillation, weighted equally with α = 0.5.The authors also test reverse KL and combined forward- and reverse-KL objectives.Source: §3.2 · pp. 3–4
3.3 Experimental Setup
- The experimental setup investigates how training-template choice affects students’ safety-alignment preservation during distillation.The model pairs use larger Instruct variants as teachers and smaller Instruct variants as students.Source: §3.3 · p. 4, §3.3.1 · p. 4
3.3.1 Models.
- Experiments use LLaMA-3, Gemma-2, and Qwen-2.5 teacher–student pairs, selected for their pre-training filtration and post-training safety alignment.Each family pairs a larger Instruct teacher with a smaller Instruct student.Source: §3.3.1 · p. 4
3.3.2 Datasets.
- Distillation uses the Databricks Dolly 15k instruction-following dataset, while four adversarial benchmarks are reserved for safety evaluation.The safety benchmarks are AdvBench, JailbreakBench, HarmBench, and SORRY-Bench.Source: §3.3.2 · p. 4
3.3.3 Training and Evaluation
- Utility is average ROUGE-L across four instruction-following sets, while safety is measured by benchmark ASR; evaluation uses the standard chat template for every model.Students are fine-tuned with LoRA, and the fixed inference template makes training-template choice the condition that differs.Source: §3.3.3 · p. 4
4 Results
Distilling on benign instruction-following data increases safety-benchmark attack success rates, with greater degradation under chat-template than non-chat-template distillation. Chat-template KD also yields larger utility gains, making template choice a safety–utility trade-off.
4.1 Distillation generally erodes safety.
- Benign-data KD increases ASR on most safety benchmarks across all three student models, with maximal degradation almost tripling vulnerability in some cases.The reported maximal cases are HarmBench for LLaMA and SORRY-Bench for Gemma.Source: §4.1 · p. 4
4.2 Template Choice Shapes Safety Degradation
- Chat-template KD produces larger ASR increases than non-chat KD: Gemma SORRY-Bench +35.32% vs +3.14%, and Qwen HarmBench +13.12% vs +0.12%.Table 1 summarizes student safety and utility under the different prompt templates.Source: Table 1 · p. 5, §4.2 · p. 5
- Non-chat KD reduces safety degradation but does not fully preserve refusal behavior; LLaMA still has a +17.63% HarmBench ASR increase.Its utility gains are smaller than those from chat-template KD.Source: §4.2 · p. 5
4.3 The safety-utility trade-off.
- KD improves instruction-following utility under both templates, but chat-template KD brings larger utility gains alongside the largest safety costs.Non-chat KD offers smaller utility gains while better preserving safety.Source: §4.3 · p. 5
- For LLaMA, non-chat KD has lower utility than SFT Chat (24.75 vs. 29.10) yet higher HarmBench ASR (29.81% vs. 22.50%), alongside better-preserved refusal representations.This comparison is inconsistent with lower ASR being explained simply by weaker instruction-following.Source: §4.3 · pp. 5–6
5 Analysis
The analysis tests how student and teacher prompt formats, internal refusal representations, chat-template exposure, and evaluation formatting relate to safety degradation during KD. Across these analyses, matched chat-template distillation produces the strongest reported degradation, while non-chat KD better preserves refusal geometry and prompt mixing only attenuates the loss.
5.1 KD Prompt Template Drives the Degradation
- Using chat format only for the teacher does not meaningfully degrade safety, while using it only for the student causes a small, consistent regression across both model families.The authors attribute the asymmetry to the student template shaping the input distribution used to compute gradients.Source: §5.1 · p. 6
- LLaMA HarmBench increases by +27.57% and Gemma SORRY-Bench by +35.32% under matched chat/chat KD, exceeding changes when only one side uses chat formatting.Decoupling either side removes most of the loss.Source: §5.1 · p. 6
5.2 Mechanistic Analysis
- Across Gemma, LLaMA, and Qwen, chat-template KD lowers refusal-direction similarity and projection gaps, while non-chat KD better preserves the base model’s refusal geometry.Figure 2’s PCA projections also show a larger shift under chat-template KD; the reported representational pattern accompanies higher attack success rates.Source: §5.2 · p. 7, Table 3 · p. 7, Figure 2 · p. 7
5.3 Template Mixing Provides Limited Mitigation
- As chat-template share rises, utility increases modestly while harmful-response susceptibility rises more; mixing never reduces ASR below pure non-chat KD.LLaMA and Gemma AVG utility rise from 24.75 to roughly 29 and from 23.63 to roughly 30, respectively.Source: §5.3 · p. 8, Table 4 · p. 8
5.4 Robustness to Prompt Formatting
- Under non-chat inference, chat-template KD still has higher ASR than the corresponding base across all three model families and both benchmarks, though degradation magnitude varies by format.Source: §5.4 · p. 8, Table 5 · p. 8
- Removing BOS/EOT tokens leaves KD-Chat less safe than the base across LLaMA, Gemma, and Qwen; role-marker and raw-query ablations vary by model.No single control-token component consistently explains the degradation across families.Source: §5.4 · p. 9, Table 6 · p. 9
6 Discussion
Benign-task KD degrades safety, with chat-template distillation producing greater and faster increases in harmful-query ASR than non-chat KD. The authors interpret this pattern as template-related amplification and suggest decoupling training and deployment templates, while noting that the recommendation remains to be verified beyond their data and benchmarks.
6 Discussion
- Chat-template KD produces faster and larger ASR increases than non-chat KD, alongside changes in refusal-related representations and persistence across inference formats and token ablations.The reported pattern increases with chat-template exposure and is not merely an evaluation-template artifact.Source: §6 · p. 9
- The authors hypothesize that repeated optimization on benign chat-formatted responses may weaken safety-relevant associations between structural role tokens and refusal behavior.This interpretation is consistent with larger shifts in refusal-related geometry under chat-template KD than under non-chat-template KD.Source: §6 · p. 9
- The authors suggest non-chat-template KD with standard chat inference when safety is the priority, but say robustness beyond benign instruction-following data and their benchmarks remains unverified.They characterize this training–deployment asymmetry as unusual in practice.Source: §6 · p. 9
7 Conclusion
The paper reports that benign-task KD can erode student safety alignment, with prompt templates amplifying that degradation. It concludes that preserving refusal behavior while improving utility without explicit safety supervision remains an open question.
7 Conclusion
- Benign-task KD can erode student safety alignment, while improving task utility and increasing harmful-compliance vulnerability across model families.The authors describe a potential conflict between instruction-following optimization and preserving the refusal behavior already present in aligned students.Source: §7 · p. 9
- Prompt templates are identified as a key training-time factor that amplifies safety degradation during KD.Source: §7 · p. 9
- Whether fine-tuning and distillation can preserve refusal geometry without explicit safety supervision while improving utility remains a future-work question.Source: §7 · p. 9
Limitations
The study identifies three limitations: its benign instruction-following data scope, LoRA-only distillation, and reliance on automated safety judges.
Dataset.
- Distillation uses only benign instruction-following data, leaving template-driven degradation in code, math, and reasoning settings untested.The study restricts its scope to isolate prompt-formatting effects from domain-induced distribution shifts.Source: Limitations · p. 10
Beyond LoRA.
- All experiments use LoRA-based distillation; whether full-parameter distillation shows similar degradation, and at what magnitude, remains untested.The authors expect the effect to appear under full-parameter training but note its magnitude may differ.Source: Limitations · p. 10
Evaluator Bias.
- Automated judges may miss forms of harmful content, although fixed judges keep comparisons consistent within each benchmark despite possible differences in absolute ASR values.Direct comparison across judges is left for future work.Source: Limitations · p. 10
Ethical Considerations
The paper reports research use of public datasets and models without human subjects, and its appendices point to supporting implementation, dataset, and prompt-template details.
Ethical Considerations
- The experiments use publicly available datasets and models, involve no human subjects, and use harmful or offensive evaluation data solely for research.Source: Ethical Considerations · p. 10
A.1 Hyperparameter details
- Table 7 summarizes distillation hyperparameters; training uses BF16 mixed precision, while instruction-following evaluation uses temperature 1.0 and top_p 0.7.Source: §A.1 · p. 13, Table 7 · p. 13
A.2 Dataset Details
- Table 8 provides statistics for downstream instruction-following training data and safety-evaluation benchmarks.Source: §A.2 · p. 13, Table 8 · p. 13
A.3 Prompt Template
- Table 9 shows an example of each prompt template used during distillation.Source: §A.3 · p. 13
A.4 Safety Evaluation
Across the reported evaluations, chat-template distillation is associated with greater safety degradation than non-chat distillation, while also producing faster early utility gains. The pattern extends across alternative distillation objectives and teacher and student models.
A.4 Safety Evaluation
- AdvBench uses 520 harmful behaviors judged by LLaMA-3-Guard-8B, JailbreakBench uses 200 harmful prompts, and SORRY-Bench uses 440 prompts judged by a fine-tuned Mistral model.AdvBench and JailbreakBench results are averaged over two seeds; SORRY-Bench uses a 44-class taxonomy.Source: §A.4 · p. 13
- HarmBench evaluates 320 behaviors across standard, copyright, contextual, and multimodal categories, using five generated test cases per behavior and ASR as the safety measure.Lower ASR indicates safer behavior; the evaluation uses the provided LLaMA-2-13b-cls classifier.Source: §A.4 · p. 13
C Generalization to other distillation objective
- Under RKL and FKL+RKL, chat-template distillation yields higher ASR and higher average utility than non-chat distillation across LLaMA, Gemma, and Qwen.Under RKL, HarmBench ASR rises from 12.56 to 26.63 for LLaMA and from 18.38 to 29.38 for Qwen; Gemma SORRY-Bench ASR rises from 18.86 to 41.29.Source: C Generalization to other distillation… · p. 14, Table 11 · p. 15
D Teacher Model Performance
- Chat-template SFT degrades safety across teacher and student models and both model families and scales, whereas non-chat SFT largely preserves baseline safety.Examples include Gemma 9B SORRY: +34.31 versus +0.53, and LLaMA 8B HarmBench: +7.00 versus −0.06.Source: D Teacher Model Performance · p. 14
E Chat templates accelerate vulnerability.
- Chat-template KD produces sharper early increases in both ROUGE-L utility and ASR, while non-chat KD follows a more gradual trajectory with slower ASR increases.The reported trajectory is for the student LLaMA model.Source: Figure 3 · p. 14, E Chat templates accelerate… · p. 14
B Safety-Utility
- Utility is measured by average ROUGE-L across DollyEval, SelfInst, VicunaEval, and S-NI, while SORRY-Bench scores indicate safety, with lower values safer.The reported decoding temperature is 1.0 with top_p = 0.7.Source: B Safety-Utility · p. 14, Table 10 · p. 15
F Preliminary causal intervention on refusal direction.
Preliminary interventions on refusal directions changed measured safety behavior, though one LLaMA ablation lowered ASR through incoherent outputs rather than improved safety. Inference-format ablations show that no single chat-template component fully accounts for the degradation.
F Preliminary causal intervention on refusal direction.
- Adding the base model’s refusal direction to KD-Non-Chat reduces ASR across all three model families; ablating the learned direction raises ASR for Gemma and Qwen.The intervention evaluates 40 harmful SORRY-Bench prompts; for LLaMA, ablation instead lowers ASR because more outputs become repetitive or off-topic.Source: F Preliminary causal intervention on… · p. 15, Table 13 · p. 16
G Full token ablations comparison
- KD-Chat remains less safe than its base model in several HarmBench inference-format settings, especially after BOS/EOT removal, while role-tag and raw-query results vary by model.The compared formats include standard chat, BOS/EOT removal, role-tag removal, non-chat, and raw harmful queries; no single chat component fully explains the degradation.Source: G Full token ablations comparison · p. 16, Table 14 · p. 17
H Full Token Ablation Comparison
- The same inference-format comparison finds that KD-Chat remains less safe than the base model in several settings, particularly after BOS/EOT removal, with role-tag and raw-query outcomes model dependent.Inference formatting can substantially modulate the magnitude of degradation.Source: H Full Token Ablation Comparison · p. 16, Table 14 · p. 17
I Use of AI Assistants
- The authors used AI assistants to polish text and debug code, while retaining responsibility for the paper’s content, ideas, and analyses.Source: I Use of AI Assistants · p. 16