United States

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&#39; 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.

ACL 2026

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

explainble ai

psycholinguistic patterns

llms

deception detection

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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

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.

Large Language Models are increasingly used as safety infrastructure for detecting harmful online content and moderating social media across multiple languages. Yet their effectiveness remains uneven across linguistic communities. This disparity reflects not only disparities in training data availability but also structural problems in annotation design. We argue that a central source of multilingual safety failure lies in the annotation gap underlying existing hate speech datasets. Most annotation guidelines and safety benchmarks are developed for English and standard language varieties, overlooking dialectal variation and culturally embedded forms of hostility. Using Arabic dialectal discourse as a case study, we show how harmful speech expressed through dialects, sarcasm, code-switching, and culturally specific expressions often remains undetected by current annotation schemes. We introduce the concept of the Multilingual Safety Annotation Gap (MSAG), identifying four sources of bias: language coverage gaps, dialect representation gaps, cultural semantic gaps, and annotation guideline gaps. We discuss implications for LLM safety alignment and outline directions for culturally grounded multilingual annotation. This paper is primarily a conceptual and methodological position paper; rather than introducing a new benchmark or empirical evaluation, we aim to formalize the MSAG as a framework for analyzing systematic weaknesses in multilingual safety annotation pipelines.

Lost in Dialect: The Annotation Gap in Multilingual LLM Safety

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

As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preserving model utility has become paramount. Recent work has explored sparse autoencoders (SAEs) to perform precise interventions on monosemantic features. However, most SAE-based methods operate at inference time, which does not create persistent changes in the model's parameters. Such interventions can be bypassed or reversed by malicious actors with parameter access. We introduce CRISP, a parameter-efficient method for persistent concept unlearning using SAEs. CRISP automatically identifies salient SAE features across multiple layers and suppresses their activations. We experiment with two LLMs and show that our method outperforms prior approaches on safety-critical unlearning tasks from the WMDP benchmark, successfully removing harmful knowledge while preserving general and in-domain capabilities. Feature-level analysis reveals that CRISP achieves semantically coherent separation between target and benign concepts, allowing precise suppression of the target features.

CRISP: Persistent Concept Unlearning via Sparse Autoencoders

Controlling undesirable LLM behaviors typically requires costly fine-tuning, while existing inference-time steering methods lack fine-grained adaptivity. We introduce a lightweight, trainable controller network for adaptive inference-time control. The controller observes intermediate LLM activations to predict a global scaling factor and layer-specific weights, which dynamically modulate a pre-computed ``refusal direction'' vector. Trained on harmful and benign prompts, the controller learns to apply nuanced, layer-aware steering selectively. Experiments on Llama and Mistral models show our method significantly increases refusal rates on safety benchmarks like ToxicChat, outperforming existing approaches without altering the original model parameters.

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

Distilling multi-step reasoning abilities from large language models (LLMs) into compact student models remains challenging due to noisy rationales, hallucinated supervision, and static teacher–student interactions. Existing reasoning distillation methods, including mentor-based approaches, predominantly operate in an open-loop manner, implicitly assuming uniform teacher reliability and consequently propagating erroneous intermediate reasoning. We propose \textbf{GateKD}, a confidence-gated closed-loop distillation framework that enables robust reasoning transfer by treating the teacher as a dynamic gatekeeper rather than a static oracle. GateKD introduces three complementary mechanisms: (i) confidence-gated soft supervision that selectively distills reliable predictive signals, (ii) gated hidden-state evolution that aligns intermediate representations only when teacher confidence is high, and (iii) reliability-filtered attention distillation that preserves stable reasoning structures while suppressing noisy patterns. These components jointly form a closed feedback loop in which teacher confidence continuously modulates the distillation process, reducing hallucination transfer and stabilizing student reasoning. Extensive experiments across commonsense, logical, and symbolic reasoning benchmarks, using T5 and Flan-T5 backbones of varying sizes, demonstrate that GateKD consistently outperforms strong open-loop distillation baselines. Notably, GateKD yields substantial gains in logical and symbolic reasoning, remains robust under low-resource distillation settings, and shows clear performance degradation when any gating component is removed. Our results highlight that confidence-gated closed-loop supervision is critical for building reliable and scalable small reasoning models.

GateKD: Confidence-Gated Closed-Loop Distillation for Robust Reasoning

Modern Large Language Models (LLMs) rely on extensive safety alignment, yet the mechanistic basis of refusal remains opaque. In this work, we investigate whether safety compliance is a deep semantic decision or a manipulable linear feature. We introduce Contrastive Logit Steering (CLS), a zero-optimization framework that isolates the "refusal direction" by contrasting hidden states derived from safe and unrestricted system prompts. Unlike representation engineering methods that intervene on internal activations, CLS operates directly on the output distribution, serving as a diagnostic probe for alignment fragility. When coupled with prefix injection to bypass initial refusal reflexes, this method induces a phase transition where guardrails collapse. Our experiments on 7 model families reveal that safety implementation is architecturally deterministic. While models like Llama-3.1 exhibit a "Late Decision" topology that is easily bypassed by CLS (reaching 95% ASR in milliseconds), others like Qwen-2.5 demonstrate "Early Divergence" by integrating safety mid-computation. Direct comparison with established activation-level steering methods shows that CLS achieves substantially higher attack success rates on Llama 2 (73% vs. 22.6%) and Qwen 7B (91% vs. 79.2%), demonstrating that logit-level intervention exposes alignment vulnerabilities that hidden-state methods underestimate. Beyond attacks, we show that this linearity enables bidirectional control: inverting the steering vector "hardens" models against jailbreaks without retraining. Our findings suggest that current alignment techniques create a steerable "safety axis" that serves as both a critical vulnerability and a precise primitive for defense.

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.

The Conservative AI: Diagnosing Hold Bias and Reliability Limits in Persona-Based Monetary Policy Simulation

Existing work on LLM confidence has been predominantly framed as an estimation problem of uncertainty quantification and calibration, while the operational question of how confidence should be used to drive system decisions remains fragmented. This paper presents a comprehensive overview of $\textbf{confidence utilization}$ in LLMs. We first establish a unified definition of confidence as a control signal, decomposed along three axes (source, granularity, and functional role) to clarify existing ambiguities. We then propose a systematic taxonomy organized along the LLM lifecycle: confidence-aware training, confidence-driven inference, confidence-guided model selection, confidence-gated retrieval, and confidence-based risk management. For each family we compare methods on shared axes and highlight cross-cutting trade-offs, and finally outline open problems and promising directions toward jointly reliable, efficient, and trustworthy LLM systems.

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