Singapore

Large language models (LLMs) have demonstrated remarkable performance across diverse tasks by encoding vast amounts of factual knowledge. However, they are still prone to hallucinations, generating incorrect or misleading information, often accompanied by high uncertainty. Existing methods for hallucination detection primarily focus on quantifying internal uncertainty, which arises from missing or conflicting knowledge within the model. However, hallucinations can also stem from external uncertainty, where ambiguous user queries lead to multiple possible interpretations. In this work, we introduce **Semantic Volume**, a novel mathematical measure for quantifying both external and internal uncertainty in LLMs. Our approach perturbs queries and responses, embeds them in a semantic space, and computes the Gram matrix determinant of the embedding vectors, capturing their dispersion as a measure of uncertainty. Our framework provides a generalizable and unsupervised uncertainty detection method without requiring internal access to LLMs. We conduct extensive experiments on both external and internal uncertainty detections, demonstrating that our Semantic Volume method consistently outperforms existing baselines in both tasks. Additionally, we provide theoretical insights linking our measure to differential entropy, unifying and extending previous sampling-based uncertainty measures such as the semantic entropy. Semantic Volume is shown to be a robust and interpretable approach to improving the reliability of LLMs by systematically detecting uncertainty in both user queries and model responses.

AAAI 2026

Semantic Volume: Quantifying and Detecting Both External and Internal Uncertainty in LLMs

llm hallucination

uncertainty quantification

large language models

### 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

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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.

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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.

Composed Image Retrieval (CIR) is a challenging image retrieval paradigm that enables to retrieve target images based on multimodal queries consisting of reference images and modification texts. Although substantial progress has been made in recent years, existing methods assume that all samples are correctly matched. However, in real-world scenarios, due to high triplet annotation costs, CIR datasets inevitably contain annotation errors, resulting in incorrectly matched triplets. To address this issue, the problem of Noisy Triplet Correspondence (NTC) has attracted growing attention. We argue that noise in CIR can be categorized into two types: cross-modal correspondence noise and modality-inherent noise. The former arises from mismatches across modalities, whereas the latter originates from intra-modal background interference or visual factors irrelevant to the coarse-grained modification annotations. However, modality-inherent noise is often overlooked, and research on cross-modal correspondence noise remains nascent. To tackle above issues, we propose the Invariance and discrimiNaTion-awarE Noise neTwork (INTENT), comprising two components: Visual Invariant Composition and Bi-Objective Discriminative Learning, specifically designed to handle the two-aspect noise. The former applies causal intervention on the visual side via Fast Fourier Transform (FFT) to generate intervened composed features, enforcing visual invariance and enabling the model to ignore modality-inherent noise during composition. The latter adopts collaborative optimization with both positive and negative samples, and constructs a scalable decision boundary that dynamically adjusts decisions based on the loyalty degree, enabling robust correspondence discrimination. Extensive experiments on two widely used benchmark datasets demonstrate the superiority and robustness of INTENT.

INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval

Behavioral game theory models serve two purposes: yielding insights into how human decision-making works, and predicting how people would behave in novel strategic settings. A system called GameNet represents the state of the art for predicting human behavior in the setting of unrepeated simultaneous-move games, combining a simple "level-k" model of strategic reasoning with a complex neural network model of non-strategic "level-0" behavior. Although this reliance on well-established ideas from cognitive science ought to make GameNet interpretable, the flexibility of its level-0 model raises the possibility that it is able to emulate strategic reasoning. In this work, we prove that GameNet's level-0 model is indeed too general. We then introduce ElementaryNet, a novel neural network that is provably incapable of expressing strategic behavior. We show that these additional restrictions are empirically harmless, leading ElementaryNet to statistically indistinguishable predictive performance vs GameNet. We then show how it is possible to derive insights about human behavior by varying ElementaryNet's features and interpreting its parameters, finding evidence of iterative reasoning, learning about the depth of this reasoning process, and showing the value of a rich level-0 specification.

ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games

In human-AI decision making, designing AI that complements human expertise has been a natural strategy to enhance human-AI collaboration, yet it often comes at the cost of decreased AI performance in areas of human strengths. This can inadvertently erode human trust and cause them to ignore AI advice precisely when it is most needed. Conversely, an aligned AI fosters trust yet risks reinforcing suboptimal human behavior and lowering human-AI team performance. In this paper, we start by identifying this fundamental tension between performance-boosting (i.e., *complementarity*) and trust-building (i.e., *alignment*) as an inherent limitation of the traditional approach for training a single AI model to assist human decision making. To overcome this, we introduce a novel, *human-centered adaptive AI ensemble* that strategically toggles between two specialist AI models—the aligned model and the complementary model—based on contextual cues, using an elegantly simple yet provably near-optimal *Rational Routing Shortcut* mechanism. Comprehensive theoretical analyses elucidate why the adaptive AI ensemble is effective and when it yields maximum benefits. Moreover, extensive simulations and real-world experiments show that when humans are assisted by the adaptive AI ensemble in decision making, they can achieve significantly higher decision performance than when they are assisted by single AI models that are trained to either optimize for its independent performance or even the human-AI team performance.

Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration

Large language models (LLMs) demonstrate remarkable capabilities in various complex language tasks, yet they face significant reliability challenges, including factual inaccuracies and generated biases. Uncertainty quantification (UQ) plays a pivotal role in assessing model trustworthiness, particularly for high-stakes applications. However, current UQ methods for LLMs encounter computational efficiency bottlenecks due to their reliance on extensive sampling or external model invocations. In this work, we introduce a novel, sampling-free uncertainty quantification framework centered on hidden layer representation analysis. Our method facilitates real-time uncertainty quantification by modeling hierarchical internal semantic dynamics during the generation process. Through comprehensive experiments on multiple QA datasets and diverse model scales, we show that our approach consistently outperforms existing uncertainty quantification techniques in distinguishing correct from incorrect generations. Our results reveal that analyzing the dynamic evolution of hidden states provides a potent and computationally efficient signal for uncertainty quantification, directly from the model's internal workings, surpassing methods that depend solely on output probabilities or approximations via multiple samples.

Sampling-Free Uncertainty Quantification via Hidden State Dynamics in Language Models

Generative self-supervised learning on graphs has emerged as a popular learning paradigm and demonstrated its efficacy in handling non-Euclidean data. However, several remaining issues limit the capability of existing methods: 1) the disregard of uneven node significance in masking, 2) the underutilization of holistic graph information, 3) the ignorance of semantic knowledge in the representation space due to the exclusive use of reconstruction loss in the output space, and 4) the unstable reconstructions caused by the large volume of masked contents. In light of this, we propose ACE-GSL, an adaptive and context-rich graph self-supervised learning framework to address these issues from the perspectives of adaptivity, integrity, complementarity, and consistency. 
Specifically, we first develop an adaptive feature mask generator to account for the unique significance of nodes and sample informative masks (adaptivity). We then design a ranking-based structure reconstruction objective joint with feature reconstruction to capture holistic graph information and emphasize the topological proximity between neighbors (integrity). After that, we present a bootstrapping-based similarity module to encode the high-level semantic knowledge in the representation space, complementary to the low-level reconstruction in the output space (complementarity). Finally, we build a consistency assurance module to provide reconstruction objectives with extra stabilized consistency targets (consistency). Extensive experiments demonstrate that ACE-GSL achieves state-of-the-art performance over 30 methods on 20 datasets across 3 tasks.

Adaptive and Context-rich Generative Self-supervised Learning on Graphs

Real-world text classification datasets frequently exhibit long-tail distributions, where numerous classes have sparse data, significantly degrading model performance on these underrepresented categories. While Large Language Models (LLMs) offer promise for data augmentation, existing methods often produce semantically limited samples, neglect "implicit long-tails" (sparse sub-patterns within classes), and lack cost-effective optimization. To address these challenges, we propose \textbf{LADA-LD (LLM-driven Adaptive Data Augmentation framework for Long-tail Distributions)}, a novel cognitive-inspired framework emulating the human learning process of "recognize, explore, generate, and optimize." LADA-LD systematically enhances augmented data diversity by first detecting both explicit and implicit long-tails. It then employs an LLM for diversity-aware planning of augmentation strategies, followed by conditional generation. A low-overhead quality and diversity validator filters the synthetic data, and an adaptive incremental sampler refines future augmentation efforts based on proxy model feedback, ensuring efficient and budget-aware optimization. Extensive experiments on multiple public text classification datasets demonstrate LADA-LD's superiority over state-of-the-art methods in improving tail-class performance and overall model robustness by generating more diverse and high-fidelity augmented data. The source code is available at \url{https://anonymous.4open.science/r/DEALT-FAD0/}.

DEALT: LLM-driven Diversity-Enhanced Data Augmentation for Long-Tail Text Classification

Infrared and visible image fusion aims to integrate complementary multi-modal information into a single fused result. However, existing methods 1) fail to account for the degradation visible images under adverse weather conditions, thereby compromising fusion performance; and 2) rely on fixed network architectures, limiting their adaptability to diverse degradation scenarios. To address these issues, we propose a one-stop degradation-aware image fusion framework for multi-degradation scenarios driven by a large language model (MdaIF). Given the distinct scattering characteristics of different degradation scenarios (e.g., haze, rain, and snow) in atmospheric transmission, a mixture-of-experts (MoE) system is introduced to tackle image fusion across multiple degradation scenarios. To adaptively extract diverse weather-aware degradation knowledge and scene feature representations, collectively referred to as the semantic prior, we employ a pre-trained vision-language model (VLM) in our framework. Guided by the semantic prior, we propose degradation-aware channel attention module (DCAM), which employ degradation prototype decomposition to facilitate multi-modal feature interaction in channel domain. In addition, to achieve effective expert routing, the semantic prior and channel-domain modulated features are utilized to guide the MoE, enabling robust image fusion in complex degradation scenarios. Extensive experiments validate the effectiveness of our MdaIF, demonstrating superior performance over SOTA methods. The code will be released in the future.

MdaIF: Robust One-Stop Multi-Degradation-Aware Image Fusion with Language-Driven Semantics

Recent advances in Deep Learning (DL) have improved multivariate time series (MTS) classification and regression by capturing complex patterns, but their lack of transparency hinders decision-making. Explainable AI (XAI) methods offer partial insights, yet often fall short of conveying the full decision space. Counterfactual Explanations (CE) provide a promising alternative, but current approaches typically prioritize either accuracy or sparsity -- rarely both -- limiting their practical value. To address this, we propose CONFETTI, a novel multi-objective CE method for MTS. CONFETTI uses Class Activation Maps (CAMs) to identify key subsequences, locates a counterfactual target, and optimally modifies the time series to balance prediction confidence and sparsity. This method provides actionable insights with minimal changes, improving interpretability, and decision support. CONFETTI is evaluated on seven MTS datasets from the UEA archive, demonstrating its effectiveness in various domains. CONFETTI consistently outperforms state-of-the-art CE methods in its optimization objectives, and in six other metrics from the literature, achieving, for example, at least 10% higher confidence while improving sparsity in at least 40%.

Counterfactual eXplainable AI (XAI) Method for Deep Learning-Based Multivariate Time Series Classification

Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses. This approach significantly expands LLM capabilities by leveraging vast, up-to-date external knowledge. However, this reliance on external knowledge makes RAG systems vulnerable to corpus poisoning attacks that manipulate generated outputs via poisoned document injection. Existing poisoning attack strategies typically treat the retrieval and generation stages as disjointed, limiting their effectiveness. We propose Joint-GCG, the first framework to unify gradient-based attacks across both retriever and generator models through three innovations: (1) Cross-Vocabulary Projection for aligning embedding spaces, (2) Gradient Tokenization Alignment for synchronizing token-level gradient signals, and (3) Adaptive Weighted Fusion for dynamically balancing attacking objectives. Evaluations demonstrate that Joint-GCG achieves at most 25% and an average of 5% higher attack success rate than previous methods across multiple retrievers and generators. While optimized under a white-box assumption, the generated poisons show unprecedented transferability to unseen models. Joint-GCG's innovative unification of gradient-based attacks across retrieval and generation stages fundamentally reshapes our understanding of vulnerabilities within RAG systems.

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems

Certified defenses aim to provide provable robustness against attacks. Models certified against adversarial attacks provide $l_p$-bounded guarantees on the absence of adversarial manipulation of inputs for predicted labels. We study the potential for malicious exploitation of certification frameworks to better understand the limits of guarantee provisions. The objective is to not only mislead a classifier but also manipulate the certification process to generate robustness certificate guarantee for an adversarial input—*certificate spoofing*. A recent study in ICLR demonstrated crafting large perturbations can shift inputs far into regions cable of generating a certificate for an incorrect class. Our study investigates if perturbations needed to cause a misclassification and yet coax a certified model into issuing deceptive, large robustness radii can still be imperceptible. We explore the idea of region-focused adversarial examples to demonstrate imperceptible perturbations capable of spoofing certificates and achieving certification radii larger than the source class—*ghost certificates*. Extensive evaluations with *ImageNet* demonstrate the ability to effectively bypass state-of-the-art certified defenses. Our work raises new questions regarding the safe deployment of systems with certified defenses and current robustness verification methods, while underscoring the need to invest efforts to better understand and appreciate the robustness guarantees of certified models.

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