Singapore

Ensuring consistently high-quality training data is essential for developing reliable machine learning systems. Recent research demonstrates that incorporating human supervision into training set debugging effectively improves model performance, especially for text classification tasks.
However, such methods often prove inapplicable to image understanding tasks, where inherently unstructured pixel data presents challenges in understanding and correcting biases.
Inspired by human-AI alignment, we introduce AACA (Attribution Analysis-based Concept Alignment), a human-in-the-loop framework that mitigates bias in the training set by aligning the concepts used by humans and AI during the decision-making process. 
Specifically, AACA comprises two primary stages: interpretable data bug discovery and targeted data augmentation.
During the data bug discovery stage, AACA identifies confounded and valid concepts to explain why prediction failure occurs and what concept the model should focus, using interpretability methods and human annotation. 
In the stage of targeted data augmentation, AACA adopts these concept-level attributions as clues to synthesize debugging instances via text-to-image generative model. 
The initial model is then retrained on the augmented set to correct prediction failures. 
Comparative experiments conducted on crowdsourced annotations and real-world datasets demonstrate that AACA can accurately identifies data bugs and effectively repairs prediction failures, thereby significantly improving prediction performance.

AAAI 2026

Attribution Analysis-based Concept Alignment: A Human-in-the-loop Data Debugging Framework

data debugging

concept alignment

human-in-the-loop ai

trustworthy ai

crowdsourcing

## 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.

poster

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.

Multi-label learning is a practical machine learning paradigm dealing with instances associated with multiple labels simultaneously. Most existing multi-label learning studies are designed under the closed-world assumption, i.e. a fixed size of label space. However, it encounters significant difficulties in open-set scenarios, where test data may contain unknown labels absent from the training set to be recognized. Existing method typically tackles this challenging problem through sub-labeling approximations and prototype-based comparisons, which often overlooks the implicit information carried by unknown labels. To address this, we propose a novel framework CREM, i.e. Classifier-induced REciprocal point for Multi-label open-set recognition, which rethinks the above problem from the reciprocal point perspective. Specifically, reciprocal points are formulated by explicitly constraining the opposition feature space to a learnable bounded margin. Then reciprocal points can be induced through the classifier with the instance-wise bias eliminated. Subsequently, a unified optimization framework is introduced to jointly facilitate the classifier and reciprocal points induction. Extensive experiments demonstrate the effectiveness and superiority of the proposed CREM approach in the multi-label open-set recognition paradigm.

Classifier-induced Reciprocal Points for Multi-label Open-set Recognition

This paper systematically examines nation-level biases exhibited by Large Language Models (LLMs) within the domain of International Relations (IR). Leveraging historical records from the United Nations Security Council (UNSC), we developed a bias evaluation framework comprising three distinct tests to explore nation-level bias in various LLMs, with a particular focus on the five permanent members of the UNSC. Experimental results show that, even with the general bias patterns across models (e.g., favorable biases toward the western nations, and unfavorable biases toward Russia), these still vary based on the LLM. Notably, even within the same LLM, the direction and magnitude of bias for a nation change depending on the evaluation context. This observation suggests that LLM biases are fundamentally multidimensional, varying across models and tasks. We also observe that models with stronger reasoning abilities show reduced bias and better performance. Building on this finding, we introduce a debiasing framework that improves LLMs’ factual reasoning combining Retrieval-Augmented Generation with Reflexion-based self-reflection techniques. Experiments show it effectively reduces nation-level bias, and improves performance, particularly in GPT-4o-mini and LLama-3.3-70B. Our findings emphasize the need to assess nation-level bias alongside performance when applying LLMs in the IR domain.

“As Eastern Powers, I Will Veto.”: An Investigation of Nation-Level Bias of Large Language Models in International Relations

Exploration in sparse-reward tasks remains a fundamental challenge in multi-agent reinforcement learning (MARL) due to complex inter-agent interactions and the expansive exploration space. To address this issue, we propose Targeted Multi-Agent Exploration (TMAE), a novel framework that uncovers the causal relationships between the state space and the reward function, thereby reducing the exploration space and enabling more targeted exploration. Specifically, we construct a structural causal model (SCM) to model the causality between sub-state variables and sparse rewards, providing a robust analytical foundation for subsequent causal inference. Through counterfactual causal intervention, TMAE identifies the most critical subspaces for discovering rare but pivotal events while filtering out confounders. By incorporating these causal insights into the exploration process, TMAE prioritizes subspaces with stronger causal effects on sparse rewards, significantly enhancing exploration efficiency. We evaluate TMAE on a range of MARL benchmarks featuring sparse rewards, consistently demonstrating superior exploration efficiency compared to state-of-the-art methods. Furthermore, visualized causal insights derived from TMAE reveal its ability to effectively capture intricate dependencies and priorities in targeted exploration, showcasing strong alignment with prior domain knowledge.

TMAE:Learning Targeted Multi-Agent Exploration via Causal Inference

Proper quantification of predictive uncertainty is essential for the use of machine learning in safety-critical applications. Various uncertainty measures have been proposed for this purpose, typically claiming superiority over other measures. In this paper, we argue that there is no single best measure. Instead, uncertainty quantification should be tailored to the specific application. To this end, we use a flexible family of uncertainty measures that distinguishes between total, aleatoric, and epistemic uncertainty. These measures can be instantiated with specific loss functions, so-called proper scoring rules, to control their characteristics, and we show that different characteristics are useful for different tasks. In particular, we show that, for the task of selective prediction, the scoring rule should ideally match the task loss. On the other hand, for out-of-distribution detection, our results confirm that mutual information, a widely used measure of epistemic uncertainty, performs best. Furthermore, in an active learning setting, epistemic uncertainty based on zero-one loss is shown to consistently outperform other uncertainty measures.

Uncertainty Quantification for Machine Learning: One Size Does Not Fit All

Text-attributed heterogeneous graphs (TAHGs), characterized by nodes interconnected through diverse relationships and enriched with textual descriptions, are prevalent in numerous real-world applications. Recent advancements in integrating pre-trained language models (PLMs) and large language models (LLMs) with heterogeneous graph neural networks (HGNNs) have enhanced learning on TAHGs. However, the absence of standardized benchmark datasets tailored to TAHGs has impeded further progress. To bridge this gap, we propose the Text-attributed Heterogeneous Graphs Benchmark (THGB), a comprehensive collection of heterogeneous graphs from diverse domains, with each node enriched by relevant text attributes. Alongside dataset construction, we conduct extensive benchmark experiments using various graph learning methods, including GNN, PLM-GNN, and LLM-GNN approaches, for node classification and link prediction tasks. We evaluated model performance across supervised, few-shot, and zero-shot learning scenarios to assess their ability to leverage limited and unseen data. Our experiments highlight THGB's potential to improve the integration of heterogeneous structural and textual information. By providing curated datasets, robust evaluation protocols, and baseline implementations, THGB introduces a standardized benchmark and solid groundwork for TAHGs research.

THGB: A Comprehensive Benchmark for Text-attributed Heterogeneous Graphs

Retrieval-augmented generation (RAG) has greatly improved Large Language Models (LLMs) by adding external knowledge. However, current RAG-based methods face difficulties with long-context video understanding due to two main challenges. First, Current RAG-based methods for long-context video understanding struggle to effectively integrate multimodal and long-range temporal information, resulting in fragmented and context-insensitive knowledge representations. Furthermore, their retrieval mechanisms often rely on static textual matching, failing to dynamically align user queries with the most relevant video segments and leading to suboptimal downstream performance. To overcome these issues, we introduce \textbf{ViG-RAG}, a new framework to enhance long-context video understanding through structured textual knowledge grounding and multi-modal retrieval. Specifically, we segment video transcripts into structured units, extract key entities, form temporal connections and confidence for evidence, enabling coherent long-range reasoning. In this way, it utilizes a knowledge-aware grounding mechanism and a context-aware retrieval process that dynamically builds a probabilistic temporal knowledge graph to organize multi-video content. To improve retrieval accuracy, we propose a hybrid retrieval strategy for semantic and temporal features, with an adaptive distribution modeling the relevance. In this way, it achieves the optimal retrieval distribution for each query, enhancing generation efficiency by reducing unnecessary computations. On top of this, ViG-RAG uses a vision-language model to integrate semantic anchors, expanded contextual fields, and selected video frames, generating an accurate response. We evaluate ViG-RAG on several benchmarks, demonstrating that it significantly surpasses current RAG-based methods.

ViG-RAG: Video-aware Graph Retrieval-Augmented Generation via Temporal and Semantic Hybrid Reasoning

Recent advancements in multimodal large language models (MLLMs) have shown remarkable progress in video understanding. However, video MLLMs (VideoMLLMs) still suffer from hallucinations, generating nonsensical or irrelevant content. This issue partly stems from over-reliance on pre-trained knowledge, sometimes neglecting the rich visual information present in the video. Additionally, many existing methods rely on uniform frame sampling, which can overlook critical visual cues. To address these challenges, we present EchoBat, a novel approach that leverages audio information as well as video temporal and logical consistency to improve preference data construction and keyframe extraction. Our method integrates Direct Preference Optimization (DPO) to mitigate hallucinations by leveraging high-quality, contextually rich preference feedback. Specifically, we use GPT-4o to generate high-quality video descriptions and integrate visually relevant segments from Whisper-derived transcripts to construct preference responses. Correspondingly, we use the reference model itself to describe the reversed video, and use GPT-4o to flashback the text and fill in the hallucination to produce non-preferred responses. This strategy enhances the model’s ability to better understand visual content and temporal, logical relationships within videos. Furthermore, we propose an echo-layered sampling strategy for keyframe extraction from videos, which can provide more precise visual supervision compared to uniform sampling. Experimental results on the three latest video hallucination benchmarks demonstrate the effectiveness of our approach.

EchoBat: Echo-Vision Enhancement and Echo-Layered Sampling for Video LLMs Hallucination Mitigation

Growing concerns over data privacy underscore the need for deep learning methods capable of processing sensitive information without compromising confidentiality. Among privacy-enhancing technologies, Homomorphic Encryption (HE) stands out by offering post-quantum cryptographic security and end-to-end data protection, safeguarding data even during computation. Prior research on encrypted training has primarily focused on logistic regression, model fine-tuning, or relied on multi-party computation. This is largely due to the substantial computational overhead and algorithmic complexity involved in training deep Neural Networks (NNs) under HE. In this paper, we present ReBoot, the first framework to enable fully encrypted and non-interactive training of Multi-Layer Perceptrons (MLPs) using CKKS bootstrapping. ReBoot introduces a novel HE-compliant NN architecture based on local error signals, specifically designed to minimize multiplicative depth and reduce noise accumulation during training. It employs a tailored packing strategy that leverages real-number arithmetic through CKKS \textit{SIMD} operations, significantly lowering both computational and memory overhead. We evaluate ReBoot on both image and tabular benchmarks, demonstrating up to $+6.83\%$ improvement in test accuracy over existing solutions, while reducing training latency by up to $8.83\times$. ReBoot is made available to the scientific community as a public repository.

ReBoot: Encrypted Training of Deep Neural Networks with CKKS Bootstrapping

The proliferation of multimodal fake news across various domains presents a significant challenge to information ecosystems. While existing multi-domain fake news detection methods attempt to leverage data from multiple domains to improve generalization, they often suffer from a critical drawback: negative transfer. This problem arises from their common practice of indiscriminately aggregating information, failing to account for the complex, often asymmetric, relationships between domains. Consequently, knowledge from irrelevant or conflicting domains can severely degrade detection performance.

To address this challenge, we propose a novel framework named \textbf{PANDA: Prototype-driven Asymmetric Neighbor-Domain Adaptation}. PANDA is the first framework, to our knowledge, that explicitly models the directional transferability between news domains to mitigate negative transfer. At its core, PANDA learns a set of compact and representative \textit{prototypes} for each domain to encapsulate its core characteristics. Based on these prototypes, we devise a novel \textbf{P}rototype-based \textbf{A}symmetric \textbf{D}istance (PAD) metric to quantify the potential benefit of transferring knowledge from a source domain to a target one. Guided by this metric, a \textbf{G}umbel-based \textbf{N}eighbor \textbf{S}elector (GNS) dynamically identifies the most beneficial neighbor domains for each instance. Finally, a \textbf{D}omain-\textbf{C}ollaborative \textbf{A}ttention (DCA) module adaptively fuses knowledge from the selected domains. Extensive experiments on benchmark datasets demonstrate that PANDA significantly outperforms state-of-the-art methods and effectively mitigates negative transfer, showcasing its superior adaptability and robustness in real-world scenarios.

From Blind Transfer to Wise Selection: Prototype-Driven Neighbor-Domain Adaptation for Fake News Detection

The recent success of machine learning models, especially large-scale classifiers and language models, relies heavily on training with massive data. These data are often collected from online sources. This raises serious concerns about the protection of user data, as individuals may not have given consent for their data to be used in training. To address this concern, recent studies introduce the concept of unlearnable examples, i.e., data instances that appear natural but are intentionally altered to prevent models from effectively learning from them. While existing methods demonstrate empirical effectiveness, they typically rely on heuristic trials and lack formal guarantees. Besides, when unlearnable examples are mixed with clean data, as is often the case in practice, their unlearnability disappears. In this work, we propose a novel approach to constructing unlearnable examples by systematically maximising the Bayes error, a measurement of irreducible classification error. We develop an optimisation-based approach and provide an efficient solution using projected gradient ascent. Our method provably increases the Bayes error and remains effective when the unlearning examples are mixed with clean samples. Experimental results across multiple datasets and model architectures are consistent with our theoretical analysis and show that our approach can restrict data learnability, effectively in practice.

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