Singapore

Large language Model (LLM) unlearning, i.e., selectively removing information from LLMs, is vital for responsible model deployment. Differently, LLM knowledge editing aims to modify LLM knowledge instead of removing it. Though editing and unlearning seem to be two distinct tasks, we find there is a tight connection between them. In this paper, we conceptualize unlearning as a special case of editing where information is modified to a refusal or &quot;empty set&quot; 
response, signifying its removal. This paper thus investigates if knowledge editing techniques are strong baselines for LLM unlearning. We evaluate state-of-the-art (SOTA) editing methods (e.g., ROME, MEMIT, GRACE, WISE, and AlphaEdit) against existing unlearning approaches on pretrained and finetuned knowledge. Results show certain editing methods, notably WISE and AlphaEdit, are effective unlearning baselines, especially for pretrained knowledge, and excel in generating human-aligned refusal answers. To better adapt editing methods for unlearning applications, we propose practical recipes including self-improvement and query merging. The former leverages the LLM&#39;s own in-context learning ability to craft a more human-aligned unlearning target, and the latter enables ROME and MEMIT to perform well in unlearning longer sample sequences. We advocate for the unlearning community to adopt SOTA editing methods as baselines and explore unlearning from an editing perspective for more holistic LLM memory control.

AAAI 2026

Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?

model editing

trustworthiness

machine learning

## 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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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Large language Model (LLM) unlearning, i.e., selectively removing information from LLMs, is vital for responsible model deployment. Differently, LLM knowledge editing aims to modify LLM knowledge instead of removing it. Though editing and unlearning seem to be two distinct tasks, we find there is a tight connection between them. In this paper, we conceptualize unlearning as a special case of editing where information is modified to a refusal or "empty set" 
response, signifying its removal. This paper thus investigates if knowledge editing techniques are strong baselines for LLM unlearning. We evaluate state-of-the-art (SOTA) editing methods (e.g., ROME, MEMIT, GRACE, WISE, and AlphaEdit) against existing unlearning approaches on pretrained and finetuned knowledge. Results show certain editing methods, notably WISE and AlphaEdit, are effective unlearning baselines, especially for pretrained knowledge, and excel in generating human-aligned refusal answers. To better adapt editing methods for unlearning applications, we propose practical recipes including self-improvement and query merging. The former leverages the LLM's own in-context learning ability to craft a more human-aligned unlearning target, and the latter enables ROME and MEMIT to perform well in unlearning longer sample sequences. We advocate for the unlearning community to adopt SOTA editing methods as baselines and explore unlearning from an editing perspective for more holistic LLM memory control.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Public health and clinical decisions are intertwined.
Public health crises place a high burden on healthcare
facilities, forcing them to make decisions such as
maintaining quality vs. treating more people. Meanwhile,
sub-optimal clinical decisions also cause downstream
effects on communities, like discharging patients too early
might increase disease spread. My work brings a
data-centric perspective to bridge clinical decisions
within the context of infectious diseases for public
health. My work addresses multiple challenges arising from
effectively utilizing rich clinical datasets, and issues
stemming from the complexity of disease spread dynamics in
healthcare facilities. This talk will cover methods I have
developed to address these challenges with better-designed
models to optimize disease surveillance and control policy,
and new techniques for end-to-end learning with mechanistic
epidemiological models. I will conclude by discussing new
challenges and opportunities in infectious diseases and
pandemic response for computer scientists, epidemiologists,
and computational biologists.

Bridging Public Health with Clinical Decisions from a Data Centric Perspective

Adversarial Security of Financial Language Models (ASFLM) is critical as Large Language Models (LLMs) pervade high-stakes financial applications. However, LLMs face two key challenges: their vulnerability to damaging adversarial attacks and the prevalent research gap concerning robust defenses against sophisticated, semantically coherent threats. To address these, we first theoretically analyze the relationship between discrete and continuous adversarial optimization, proving the continuous optimum provides a lower bound for the discrete. This foundation supports our novel two-stage framework, ChameleonAttack. It employs Adaptive Latent-space Optimization (ALO) for potent adversarial token discovery, followed by a Semantic-Translation Module (STM) module to generate fluent, coherent, and natural-sounding adversarial text. This dual approach aims to maximize attack impact while ensuring high linguistic quality and semantic integrity for evasion. Evaluated on state-of-the-art financial LLMs (e.g., FinBERT) and standard benchmarks (e.g., Financial PhraseBank), ChameleonAttack achieves a high Attack Success Rate (ASR) of 93.4%. These results highlight significant practical vulnerabilities and underscore the urgent need for robust defense mechanisms in the financial domain.

Semantics-Preserving Adversarial Attacks on Event-Driven Stock Prediction Models

Open-vocabulary object detectors (OVODs) unify vision and language to detect arbitrary object categories based on text prompts, enabling strong zero-shot generalization to novel concepts. As these models gain traction in high-stakes applications such as robotics, autonomous driving, and surveillance, understanding their security risks becomes crucial. In this work, we conduct the first study of backdoor attacks on OVODs and reveal a new attack surface introduced by prompt tuning. We propose TrAP (Trigger-Aware Prompt tuning), a multi-modal backdoor injection strategy that jointly optimizes prompt parameters in both image and text modalities along with visual triggers. TrAP enables the attacker to implant malicious behavior using lightweight, learnable prompt tokens without retraining the base model weights, thus preserving generalization while embedding a hidden backdoor. We adopt a curriculum-based training strategy that progressively shrinks the trigger size, enabling effective backdoor activation using small trigger patches at inference. Experiments across multiple datasets show that TrAP achieves high attack success rates for both object misclassification and object disappearance attacks, while also improving clean image performance on downstream datasets compared to the zero-shot setting.

Backdoor Attacks on Open Vocabulary Object Detectors via Multi-Modal Prompt Tuning

Although large language models (LLMs) are increasingly trained using human feedback for safety and alignment with human values, alignment decisions systematically overlook human social diversity. Researchers have advocated for sociotechnical approaches integrating pluralistic values into model training. This study examines how operationalizing these approaches shapes LLM behavior through a systematic evaluation of demographic variation and design parameters within the alignment pipeline. We built an inclusive data collection process, obtaining alignment datasets from US and German participants (N = 1,095 participants, 27,375 ratings) who rated LLM responses across five dimensions: toxicity, emotional awareness, sensitivity, stereotypical bias, and helpfulness. We fine-tuned multiple Large Language Models and Large Reasoning Models using value preferences from different social groups while varying key parameters, including rating scale, disagreement handling, and preference optimization method. Results show systematic demographic effects: male participants rated responses as 18% less toxic than female participants; conservative and Black participants reported 27.9% and 58% higher emotional awareness ratings than liberal and White participants, respectively. Models finetuned on group-specific preferences exhibited distinct behaviors. Technical decisions had even greater impact on toxicity reduction: preserving rater disagreement was 1.6 times more effective than majority vote aggregation; 5-point scales outperformed binary formats by 1.23 times; Direct Preference Optimization (DPO) consistently outperformed Group-based Relative Preference Optimization (GRPO) on both toxicity and emotional awareness optimization. Meta-analysis confirms the robustness of these effects, raising a critical question: How should alignment processes balance expert-driven and user-driven signals to ensure both safety and fair representation?

Operationalizing Pluralistic Values in Large Language Model Alignment Reveals Trade-offs in Safety, Inclusivity, and Model Behavior

Present day LLMs face the challenge of managing affordance-based safety risks—situations where outputs inadvertently facilitate harmful actions due to overlooked logical implications. Traditional safety solutions, such as scalar outcome-based reward models, parameter tuning, or heuristic decoding strategies, lack the granularity and proactive nature needed to reliably detect and intervene during subtle yet crucial reasoning steps. Addressing this fundamental gap, we introduce AURA, an innovative, multi-layered framework centered around Process Reward Models (PRMs), providing comprehensive, step level evaluations across logical coherence and safety-awareness. Our framework seamlessly combines introspective self-critique, fine-grained PRM assessments, and adaptive safety-aware decoding to dynamically and proactively guide models toward safer reasoning trajectories. Empirical evidence clearly demonstrates that this approach significantly surpasses existing methods, significantly improving the logical integrity and affordance-sensitive safety of model outputs. This research represents a pivotal step toward safer, more responsible, and contextually aware AI, setting a new benchmark for alignment-sensitive applications.

AURA: Affordance-Understanding and Risk-aware Alignment Technique for Large Language Models

Large Language Models (LLMs) exhibit impressive general-purpose capabilities but also introduce serious safety risks, particularly the potential for deception as models acquire increased agency and human oversight diminishes. In this work, we present LieCraft: a novel evaluation framework and sandbox for measuring LLM deception that addresses key limitations of prior game-based evaluations. At its core, LieCraft is a novel multiplayer hidden-role game in which players select an ethical alignment and execute strategies over a long time-horizon to accomplish missions. Cooperators work together to solve event challenges and expose bad actors, while Defectors evade suspicion while secretly sabotaging missions. To enable real-world relevance, we develop 10 grounded scenarios such as childcare, hospital resource allocation, and loan underwriting that recontextualize the underlying mechanics in ethically significant, high-stakes domains. We ensure balanced gameplay in LieCraft through careful design of game mechanics and reward structures that incentivize meaningful strategic choices while eliminating degenerate strategies. Beyond the framework itself, we report results from 12 state-of-the-art LLMs across three behavioral axes: propensity to defect, deception skill, and accusation accuracy. Our findings reveal that despite differences in competence and overall alignment, all models are willing to act unethically, conceal their intentions, and outright lie to pursue their goals.

LieCraft: A Multi-Agent Framework for Evaluating Deceptive Capabilities in Language Models

3D generation and reconstruction techniques have been widely used in computer games, film, and other content creation areas. As the application grows, there is a growing demand for 3D shapes that look truly realistic. Traditional evaluation methods rely on a ground truth to measure mesh fidelity. However, in many practical cases, a shape's realism does not depend on having a ground truth reference. In this work, we propose a Shape-Realism Alignment Metric that leverages a large language model (LLM) as a bridge between mesh shape information and realism evaluation. To achieve this, we adopt a mesh encoding approach that converts 3D shapes into the language token space. A dedicated realism decoder is designed to align the language model’s output with human perception of realism. Additionally, we introduce a new dataset, RealismGrading, which provides human-annotated realism scores without the need for ground truth shapes. Our dataset includes shapes generated by 16 different algorithms on over a dozen objects, making it more representative of practical 3D shape distributions. We validate our metric's performance and generalizability through k-fold cross-validation across different objects. Experimental results show that our metric correlates well with human perceptions and outperforms existing methods, and has good generalizability.

SRAM: Shape-Realism Alignment Metric for No Reference 3D Shape Evaluation

Large Audio Language Models (LALMs) are transforming AI by directly processing and generating human language from audio. As these models proliferate in real-world applications, evaluating their performance for equitable and safe use across diverse linguistic and cultural contexts becomes paramount. This paper presents the first comprehensive study on cultural preferences and biases in LALMs across multilingual and multicultural settings. We extend existing cultural harm frameworks from text-based models to the audio domain, analysing how linguistic and cultural diversity influence LALM behaviour, sensitivity, and output quality. Our research uncovers unique challenges in interpreting cultural nuances from audio and linguistic variations. We introduce a novel multilingual audio-text dataset (10 languages, including English), \textbf{Audio Cultural Intelligence Dataset (ACID Benchmark) spanning 1315 hours in audio length}, specifically for evaluating LALM cultural biases, marking the first such examination in this emerging area. Our \textbf{comprehensive analysis includes 10 open-source and 2 closed-source models}, demonstrating significant performance disparities across languages and cultural contexts, highlighting the audio modality's influence on bias manifestation. These findings highlight the critical need to evaluate LALMs not only for technical accuracy but also for fair and culturally sensitive performance, urging the development of inclusive datasets and cultural awareness for building safer and more equitable large audio language models. The ACID benchmark will be made publicly available.

ACID Test: A Benchmark for Cultural Safety and Alignment in LALMs

Semi-supervised learning (SSL) on graphs is critical in applications where labeled data are scarce and costly, yet existing graph-based methods often degrade under extreme label sparsity or class imbalance, yielding trivial or unstable solutions. We introduce \textbf{CombCut}, the first exact combinatorial optimization framework for multi-class graph-based semi-supervised learning that operates directly on binary one-hot assignments, without any convex relaxation or heuristic volume constraints. By employing a minorization–maximization (MM) scheme, CombCut transforms each step into a structured linear assignment problem solved efficiently via network-flow algorithms. Total unimodularity guarantees integral iterates, and our theoretical analysis establishes both monotonic ascent of the true discrete objective and convergence of every limit point to a Karush–Kuhn–Tucker (KKT) stationary solution of the original combinatorial problem. Our approach requires no hyperparameter tuning and scales near-linearly in the number of vertices. Empirical evaluation on MNIST, Fashion-MNIST, and CIFAR-10 with as few as 1–5 labels per class shows that CombCut excels in worst-case labeling scenarios, significantly outperforming state-of-the-art graph-SSL baselines and yielding more stable and accurate label propagation under severe supervision constraints.

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Bridging Public Health with Clinical Decisions from a Data Centric Perspective

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