United States

Large language models (LLMs) are increasingly deployed with safety alignment mechanisms designed to prevent harmful outputs including hate speech, harassment, and unsafe instructions. However, existing safety evaluation frameworks remain heavily centered on English and standardized language varieties, creating a critical gap for languages characterized by extensive dialectal variation. Arabic provides a particularly important case: everyday communication across the Arab world occurs predominantly in regional dialects rather than Modern Standard Arabic (MSA), yet these dialects are systematically underrepresented in alignment training corpora and safety benchmarks. In this paper we introduce the Dialect Safety Gap, defined as systematic variation in LLM safety behavior across dialects of the same language. We argue that this phenomenon arises from the interaction between alignment training procedures and linguistic variation: safety alignment implicitly encodes normative patterns present in training datasets, and when dialectal forms diverge from those patterns, safety behavior degrades through lexical, morphological, and pragmatic mechanisms. We propose a formal framework grounded in algorithmic fairness that links dialect variation to alignment pipeline design, introduce both a binary DSG Score and a magnitude-aware Pairwise Dialect Inconsistency metric, and propose the Dialect-Aware Safety Evaluation Protocol (DASEP) as a practical evaluation framework. We demonstrate the feasibility of dialect-aware evaluation through a controlled, human-annotated prompt-probe experiment across five Arabic variety groups, revealing a structured gradient of safety degradation that correlates with linguistic distance from MSA.

ACL 2026

Toward Dialect-Aware Safety Evaluation for Arabic Large Language Models

llms safety

arabic

dialect

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Modern Large Language Models (LLMs) often rely on Retrieval-Augmented Generation (RAG) to access up-to-date information; however, retrieved corpora may contain misleading, outdated, or incorrect content, raising concerns about how such evidence affects model reliability. In this work, we investigate the susceptibility of LLMs to false external evidence. Existing studies have shown that poisoned external corpora can mislead LLM responses; yet, there is still a lack of studies on the effects of different evidence properties. To bridge this gap, we design comprehensive experiments along three dimensions: styles of evidence, quantity of evidence, and the semantic similarity between external messages and the model’s internal belief. We find that instructive-style evidence demonstrates the most severe performance degradation. On the other hand, we observe a steady decline in model response quality as the amount of false evidence accumulates. Finally, we show that LLMs are more susceptible to factually incorrect evidence when their semantic similarity is close to the model’s parametric knowledge.

Truth or Dare: Analyzing LLM Susceptibility to External Evidence of Varying Factuality

Deceptive reviews mislead consumers, harm businesses, and undermine trust in online marketplaces. Machine learning classifiers can learn from large amounts of data to distinguish deceptive reviews from genuine ones. However, the distinguishing features learned by these classifiers are often subtle, fragmented, and difficult for humans to interpret, which can hinder user understanding and trust. In this work, we study whether large language models (LLMs) can translate such unintuitive lexical cues into human-understandable language phenomena. We propose a conjecture-then-validate framework, and show that language phenomena obtained in this manner are empirically grounded in data, generalizable across similar domains, and more predictive than phenomena derived from LLMs' prior knowledge or in-context learning. Such phenomena can aid people in critically assessing the credibility of online reviews in environments where deception detection classifiers are unavailable.

Why is "Chicago" Predictive of Deceptive Reviews? Using LLMs to Discover Language Phenomena from Lexical Cues

As Large Language Models (LLMs) continue to advance, reinforcement learning is increasingly viewed as a promising paradigm for optimizing their general capabilities. Nevertheless, in open-ended tasks that involve complex preferences, incorporating Reward Models (RMs) into the training pipeline remains essential. Despite its importance, RM-based RL faces significant challenges: the inherent imperfections of RMs often lead to issues such as reward hacking and reward overoptimization, undermining learning reliability. In this paper, we propose RRRL (Reward-Robust Reinforcement Learning), a framework designed to improve learning stability and mitigate reward hacking in RM-based RL. Our method introduces Bayesian Reward Model Ensembles, an innovative reward modeling method to capture reward uncertainty synchronously, alongside a trade-off optimization objective to balance performance and robustness in RL, which ensures stable learning despite imperfect RMs. The proposed method effectively mitigates reward hacking and consistently surpasses baselines across diverse benchmarks, delivering improved accuracy and long-term stability. We also provide both experimental results and analysis demonstrating that the proposed method remains effective under stochastic conditions. These findings underscore the method’s potential to improve both the performance and learning stability.

Reward-Robust Reinforcement Learning in LLMs via Uncertainty Set Modeling

Instruction-tuned LLMs are able to provide *an* explanation about their output to users by generating self-explanations, without requiring the application of complex interpretability techniques. In this paper, we analyse whether this ability results in a *good* explanation. We evaluate self-explanations in the form of input rationales with respect to their plausibility to humans. We study three text classification tasks: sentiment classification, forced labour detection and claim verification. We include Danish and Italian translations of the sentiment classification task and compare self-explanations to human annotations. For this, we collected human rationale annotations for Climate-Fever, a claim verification dataset. We furthermore evaluate the faithfulness of human and self-explanation rationales with respect to correct model predictions, and extend the study by incorporating post-hoc attribution-based explanations. We analyse four open-weight LLMs and find that alignment between self-explanations and human rationales highly depends on text length and task complexity. Nevertheless, self-explanations yield faithful subsets of token-level rationales, whereas post-hoc attribution methods tend to emphasize structural and formatting tokens, reflecting fundamentally different explanation strategies.

A Systematic Comparison between Extractive Self-Explanations and Human Rationales in Text Classification

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CRISP: Persistent Concept Unlearning via Sparse Autoencoders

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Guiding Giants: Lightweight Controllers for Weighted Activation Steering in LLMs

We present SURGeLLM (Structured Understanding, Retrieval, and Generation with LLMs), a unified transformer framework for multi-task classification across structurally heterogeneous NLP domains. SURGeLLM augments a standard encoder with two lightweight mechanisms: (i) a surgical feature gate that adaptively fuses curated lexical indicators with contextual [CLS] representations, and (ii) task-conditioned prefix tokens that inject quantized feature values and task identity into the input sequence. We provide a theoretical analysis of the gate, including an approximation-error bound in terms of surgical feature alignment and an identifiability result showing that the gate degenerates to the identity map when lexical features carry no task-relevant signal. We evaluate SURGeLLM on four diverse tasks: structured physical reasoning (D1), multi-hop retrieval classification (D2), LLM-prompt attribution (D3), and human/LLM authorship detection (D4), totaling 10,764 examples. The best variant, SURGeLLM-G-RoBERTa, achieves a mean macro-F1 of 0.921, within 0.001 of the strongest baseline, while SURGeLLM variants outperform their backbone-matched baselines on retrieval, with the best D2 score reaching 0.963. Bootstrap confidence intervals (B=2,000), ablations, and feature-significance analyses further clarify when surgical lexical priors are effective and where class imbalance limits their effectiveness.

SURGELLM: Surgical Feature Injection and Task-Conditioned Prefix Learning for Unified Multi-Task NLP

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The Geometry of Refusal: Linear Instability in Safety-Aligned LLMs

We examine whether large language models (LLMs) can reliably simulate historical FOMC policy decisions and whether persona-based agentic deliberation improves performance. Using strictly time-consistent vintage economic information, we evaluate multiple state-of-the-art LLMs on a three-way Hike/Hold/Cut classification task in both single-agent and multi-agent settings. Single-LLM baselines achieve nontrivial accuracy and track broad policy regime shifts, establishing a simple but strong benchmark. However, we identify a systematic behavioral asymmetry that we term \emph{Hold bias}: models disproportionately favor Hold decisions and remain reluctant to predict Cut outcomes even during easing cycles. This conservatism is especially costly around regime turning points, where reliable adaptation matters most. We further find that standard agentic workflows, including debate and consensus-style aggregation, do not mitigate this problem and often amplify caution rather than improve accuracy. Overall, our results show that plausible deliberation is not sufficient for trustworthy decision support. Progress will require agentic systems explicitly designed to diagnose and correct structural bias, rather than merely reproducing surface-level committee interaction.

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Truth or Dare: Analyzing LLM Susceptibility to External Evidence of Varying Factuality

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Next from ACL 2026

Truth or Dare: Analyzing LLM Susceptibility to External Evidence of Varying Factuality

Similar lecture

Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora

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PRESENTATIONS

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