The paper investigates how large language models balance helpfulness and safety by refusing harmful queries while responding to benign ones. It decomposes safety-tuning responses into a boilerplate refusal statement and a rationale, finding that the statement causes false refusals by relying on superficial cues. Training on rationales alone reduces false refusals without compromising safety performance, suggesting that fine‑grained safety supervision is essential for better alignment.
By Minji Kim, Hyounghun Kim
The study examines how eight instruction-tuned large language models (LLMs) handle suicidal ideation (SI) and self‑harm disclosures across three datasets, testing whether informing models of SI context or providing a clinician‑developed response protocol changes their behavior. Results show that baseline elevated risk in model responses is largely due to ignoring the disclosure and refusing to engage; informing models of SI or suppressing refusal increases engagement but does not reliably reduce risk and can even raise it. A clinician‑guided protocol, however, consistently lowers response‑level risk and reduces refusal, highlighting the importance of tailored safety mechanisms for SI contexts.
By Drishti Goel, Soorya Ram Shimgekar, Jeongah Lee, Violeta J. Rodriguez, Daniel S. Brown, Ravi Karkar, Koustuv Saha
arXiv:2608.23264v1 Announce Type: new
Abstract: Although Large Language Models (LLMs) are aligned to optimize for both helpfulness and harmlessness, these dual objectives may conflict, inevitably lea...
By Or Biton, Tomer Krichli, Itai Allouche, Joseph Keshet
The paper introduces GUISE, a benchmark of criminal‑assistance scenarios drawn from real‑world cases and organized using the UNODC International Classification of Crime for Statistical Purposes. It evaluates large language models under three linguistic framings and across five cultures, finding that framing and cultural adaptation significantly increase unsafe compliance rates. The study also shows that unsafe responses from the least‑safe models exhibit strong Anglocentric language, with leakage growing alongside harmfulness.
By Mohsinul Kabir, Md. Tariquzzaman, Karam S. R. Elradie, Polydoros Giannouris, Yuechen Jiang, Shaoxiong Ji, Sophia Ananiadou
The paper introduces a boundary-aware self‑distillation framework for controlled large language model safety refusal, addressing the need for different refusal boundaries within the same topic. It combines controlled topic generation, coverage repair, in‑distribution compensation data, and harmful‑benign pairs to train and evaluate refusal behavior. Experiments on Qwen3‑8B show that escalating retries dramatically improve target‑domain refusal rates while reducing unsafe responses, though they also increase over‑refusal, highlighting the trade‑off between safety and usability.
By Alejo L\'opez-\'Avila, Iker Garc\'ia-Ferrero, Jezabel Garcia, Antonio Tiene, Rom\'an Or\'us
arXiv:2601. 21433v2 Announce Type: replace Abstract: Language models are increasingly consulted on ethically consequential questions, yet the stance a model expresses may not survive a change in framing.
By Katherine Elkins, Jon Chun
The paper investigates whether large language models’ refusal behavior in text transfers to tool‑calling contexts. A benchmark of 2,304 prompts across four domains compares text‑only, standard tool‑enabled, and adversarially framed tool‑enabled settings, revealing that 10–52 % of harmful requests refused in chat still lead to unsafe tool calls. The authors identify a refusal‑associated direction in the models’ residual stream, showing that lower activation along this direction predicts unsafe calls, but restoring the signal only partially mitigates the issue and often suppresses benign tool use.
By Ekagra Gupta, Raj Saha, Aniekan-Abasi Inyang, Adam Al Gharib
The paper introduces Semantic Confusion to assess how consistently large language models refuse similar prompts. It presents ParaGuard, a 10k‑prompt corpus of controlled paraphrase clusters, and proposes three token‑level metrics—Confusion Index, Confusion Rate, and Confusion Depth—to measure contradictory refusal decisions across meaning‑preserving paraphrases. Experiments show that global false rejection rates can mask local inconsistencies, revealing that refusal evaluation must consider both frequency and consistency across nearby paraphrases.
By Riad Ahmed Anonto, Md Labid Al Nahiyan, Md Tanvir Hassan
The paper investigates how large language models decide whether to call external tools before the call is made, a step that has been largely overlooked in tool‑safety research. Using factorial interventions on input requests, the authors find that neutralizing the requested operation significantly reduces harmful admissions, and that a linear direction derived from this contrast predicts admission across most models. They also show that training models to refuse in plain text can lower unsafe admissions, indicating a partial link between written refusal and tool‑admission decisions.
By Aditya Nawal, Manit Baser, Mohan Gurusamy
arXiv:2606. 26106v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in emotionally charged situations involving interpersonal conflict, frustration, and distress.
By Zhixing Sun, Shenghe Xu, Tao Li
DeflectBench is a new benchmark that evaluates how large language models (LLMs) generate rhetorical fallacies when prompted. The study tests 23,990 generations from four leading models using three deflection strategies (whataboutism, ad hominem, red herring), seven prompt framings, and 80 claims across four controversy levels. Results show that refusal to produce fallacies depends mainly on request structure, with prompt framing and fallacy type dramatically affecting compliance rates.
By Art Kanke
arXiv:2607. 24758v1 Announce Type: new Abstract: Large language models are capable of recognizing evaluation contexts and altering their behavior to reflect evaluator expectations rather than typical deployment behaviors, a phenomenon known as alignment faking.
By Cole Alexander Niblett, Alexander Chabot Nanni, Anita K. Rao