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版本更新 · v2arXiv:2606.09707

BrainSurgery: Reproducible and Flexible Declarative Weight Manipulations for Model Editing and Upcycling

Gianluca Barmina, Annemette Broch Pirchert, Andrea Blasi Núñez, Lukas Galke Poech, Peter Schneider-Kamp

原文摘要

Checkpoint transformations such as model merging, precision casting, and low-rank rewriting are commonly implemented through Python code or separate task-specific tools. We present BrainSurgery, a tool that consolidates these operations into reusable declarative YAML plans over checkpoint tensors with built-in targeting, validation, diffing, and sharded export; a Web UI supports interactive inspection and plan construction. We compare BrainSurgery with three specialized systems across 13 settings, 11 benchmarks, 8 models, 4 architectures, and model sizes from 1.7B to 32B parameters. Without use-case-specific framework customization, BrainSurgery reproduces their model edits with comparable run cost, often lower memory usage, and close downstream performance. We further perform correctness, preservation, robustness, and scaling checks. Finally, a coding-agent study shows that agents can reliably generate and review BrainSurgery plans, which are substantially shorter and more structurally consistent than equivalent Python implementations. These results support BrainSurgery as a flexible alternative to maintaining separate tools for heterogeneous checkpoint-editing tasks.

cs.LG · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.09697

PsychoSafe: Eliciting Psychologically-Informed Refusals in Large Language Models

Gianluca Barmina, Federico Torrielli, Sven Harms, Jacob Nielsen, Flavia Filesi, Felix Mächtle, Stine Lyngsø Beltoft, Peter Schneider-Kamp 等 11 位作者

原文摘要

Large language models (LLMs) routinely face requests that should be refused, creating a trade-off between helpfulness and harm prevention. However, refusals themselves can be helpful. In high-risk interactions involving crisis, coercion, or escalating intent, blunt non-compliance may prevent direct harm while still failing to support the needs of the person behind the request. We present PsychoSafe, a psychologically-informed refusal framework that reframes refusal as structured supportive communication grounded in evidence-based intervention strategies. To develop PsychoSafe, we construct a corpus of 8,019 prompt-response pairs spanning five psychologically salient risk domains and apply prompting and parameter-efficient fine-tuning to Qwen 3.5 27B. On a balanced validation set of 500 prompts, evaluated with an LLM judge and validated through human ratings, PsychoSafe prompting improves overall refusal quality by 28.1% over a generic baseline, with particularly strong gains in external resource referral (+46.8%) and psychological grounding (+34.8%), while preserving downstream performance on non-refusal tasks. Fine-tuning achieves near-perfect refusal and resource-referral rates but reduces response relevance. Additional evaluations on SORRY-Bench and XSTest show strong in-domain robustness but limited out-of-domain generalization, suggesting that future work should diversify fine-tuning data to help models apply interventions selectively rather than schematically.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.06286

LLMs Can Leak Training Data But Do They Want To? Evaluating Memorization Propensity in LLMs

Gianluca Barmina, Peter Schneider-Kamp, Lukas Galke Poech

原文摘要

Large language models can reproduce training data, but memorization evaluations mostly measure whether models can be made to do so, not whether they do so without adversarial prompting. We introduce PropMe, a framework that pairs a capability evaluation (prefix attacks) with non-adversarial propensity evaluation, and a propensity metric to relate the two. We check every generation against the model's full indexed training corpus with SimpleTrace, an open-source batch implementation of OLMoTrace span matching. We evaluate Comma, its continually pre-trained descendant DFM Decoder, and OLMo3 32B on Common Pile, Dynaword, and Dolma3, with four non-adversarial settings ranging from no prompt to sentences from unseen in-domain sources. Our results show that natural sentence prompts reproduce far less training text than prefix attacks. Generation with no prompt or a single common word, however, reproduces about as much as prefix attacks, or more, in three out of four model-corpus pairs; most of this text is duplicated licenses, code, and templates. In our setting, continual pre-training reduces long verbatim reproduction of the original corpus only modestly. More broadly, we argue that by testing both capability and propensity, we can attain a more comprehensive picture of a model's memorization behavior.

cs.CL · cs.AI

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arXiv ↗PDF ↗