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Large language models often fail to satisfy formatting instructions when they must simultaneously perform demanding tasks. We study this behavior through a prospective memory-inspired lens from cognitive psychology, using a controlled paradigm that combines verifiable formatting constraints with benchmark tasks of increasing complexity. Across three model families and over 8,000 prompts, compliance drops by 2–21% under concurrent task load. Vulnerability is highly type-dependent: terminal constraints (requiring action at the response boundary) degrade most, with drops up to 50%, while avoidance constraints remain comparatively robust. A salience-enhanced format (explicit instruction framing plus a trailing reminder) recovers much of the lost compliance, restoring performance to 90–100% in many settings. Interference is bidirectional: formatting constraints can also reduce task accuracy, with one model&#39;s GSM8K accuracy dropping from 93% to 27%. In additional stacking experiments, joint compliance declines sharply as constraints accumulate. All results use deterministic programmatic checkers, with no LLM-as-judge component, on publicly available datasets.

ACL 2026

Did You Forget What I Asked? Prospective Memory Failures in Large Language Models

instruction compliance

prospective memory

llm benchmarking

cognitive load

large language models

instruction following

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
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Large language models often fail to satisfy formatting instructions when they must simultaneously perform demanding tasks. We study this behavior through a prospective memory-inspired lens from cognitive psychology, using a controlled paradigm that combines verifiable formatting constraints with benchmark tasks of increasing complexity. Across three model families and over 8,000 prompts, compliance drops by 2–21% under concurrent task load. Vulnerability is highly type-dependent: terminal constraints (requiring action at the response boundary) degrade most, with drops up to 50%, while avoidance constraints remain comparatively robust. A salience-enhanced format (explicit instruction framing plus a trailing reminder) recovers much of the lost compliance, restoring performance to 90–100% in many settings. Interference is bidirectional: formatting constraints can also reduce task accuracy, with one model's GSM8K accuracy dropping from 93% to 27%. In additional stacking experiments, joint compliance declines sharply as constraints accumulate. All results use deterministic programmatic checkers, with no LLM-as-judge component, on publicly available datasets.

workshop paper

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Large Vision-Language Models (LVLMs) have grown increasingly powerful in recent years, but can also exhibit harmful biases. Prior studies investigating such biases have primarily focused on demographic traits related to the visual characteristics of a person depicted in an image, such as their race or gender. This has left biases related to cultural differences (e.g., religion, socioeconomic status), which cannot be readily discerned from an individual's appearance alone, relatively understudied. To address this gap, we introduce Cultural Counterfactuals: a high-quality synthetic dataset containing nearly 60k counterfactual images for measuring cultural biases related to religion, nationality, and socioeconomic status. To ensure that cultural contexts are accurately depicted, we generate our dataset using an image-editing model to place people of different demographics into real cultural context images. This enables the construction of counterfactual image sets which depict the same person in multiple different contexts, allowing for precise measurement of the impact that cultural context differences have on LVLM outputs. We demonstrate the utility of Cultural Counterfactuals for quantifying cultural biases in popular LVLMs.

Cultural Counterfactuals: Evaluating Cultural Biases in Large Vision-Language Models with Counterfactual Examples

Large language models are increasingly used in strategic and advisory contexts, yet their safety alignment is typically evaluated in English only. We test nine models from five providers and ask whether the language of a prompt can change a model's decision in a high-stakes scenario. We use single-turn game-theoretic vignettes in which a model advises a nuclear-armed nation on whether to strike a defenseless opponent. The prompt is intentionally amoral and strategically identical across languages. We find that Japanese prompts reduce launch rates in the Claude model family: Claude Sonnet 4.6 drops from 40% to 0% in scenarios where the strike is unnecessary and from 93\% to 17\% in contested scenarios, with minimal effect when the strike is strategically rational. The effect extends to Gemini Pro 3.1 (53\% to 13\%). A cross-language experiment isolates the mechanism: when instructed to reason in Japanese in an English prompt, launch rates drop from 93\% to 37\%. It is the language the model is asked to reason in, not the language of the input, that drives the effect. When reasoning in Japanese, models spontaneously generate moral vocabulary ("moral cost", "millions of lives") that is entirely absent from the prompt. Five other models show no language effect, but they launch in nearly every condition regardless of language. The effect requires a model that already hesitates in English. These results show that LLM safety behavior is language-dependent, and that evaluating in English alone can miss both risks and safeguards encoded in other languages.

Don’t Want Your LLM to Recommend Nuclear Strike? Try Asking It in Japanese

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.

Toward Dialect-Aware Safety Evaluation for Arabic Large Language Models

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

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.

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