China

Large Language Models (LLMs) are trained on massive web-crawled corpora, often containing personal information, copyrighted text, and benchmark datasets. This inadvertent inclusion in the training dataset, known as data leakage, poses significant risks and could compromise the safety of LLM outputs. Despite its criticality, existing studies do not examine how leaked instances in the pre-training data influence LLMs&#39; output and detection capabilities. In this paper, we conduct an experimental survey to elucidate the relationship between data leakage in training datasets and its effects on the generation and detection by LLMs. Our experiments reveal that LLMs often generate outputs containing leaked information, even when there is little such data in the training dataset. Moreover, the fewer the leaked instances, the more difficult it becomes to detect such leakage. Finally, we demonstrate that enhancing leakage detection through few-shot learning can help mitigate the impact of the leakage rate in the training data on detection performance.

EMNLP 2025

Investigating How Pre-training Data Leakage Affects Models&#39; Reproduction and Detection Capabilities

membership inference attack

contamination

leakage

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

IJCNLP-AACL 2025

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

Large Language Models (LLMs) are trained on massive web-crawled corpora, often containing personal information, copyrighted text, and benchmark datasets. This inadvertent inclusion in the training dataset, known as data leakage, poses significant risks and could compromise the safety of LLM outputs. Despite its criticality, existing studies do not examine how leaked instances in the pre-training data influence LLMs' output and detection capabilities. In this paper, we conduct an experimental survey to elucidate the relationship between data leakage in training datasets and its effects on the generation and detection by LLMs. Our experiments reveal that LLMs often generate outputs containing leaked information, even when there is little such data in the training dataset. Moreover, the fewer the leaked instances, the more difficult it becomes to detect such leakage. Finally, we demonstrate that enhancing leakage detection through few-shot learning can help mitigate the impact of the leakage rate in the training data on detection performance.

Investigating How Pre-training Data Leakage Affects Models' Reproduction and Detection Capabilities

poster

## 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*

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Standardized benchmarks are central to evaluating and comparing model performance in Natural Language Processing (NLP). However, Large Language Models (LLMs) have exposed shortcomings in existing benchmarks, and so far there is no clear solution. In this paper, we survey a wide scope of benchmarking issues, and provide an overview of solutions as they are suggested in the literature. We observe that these solutions often tackle a limited number of issues, neglecting other facets. Therefore, we propose concrete checklists to cover all aspects of benchmarking issues, both for benchmark creation and usage. Additionally, we discuss the advantages of adding minimal-sized test-suites to benchmarking, ensuring downstream applicability on real-world use cases.

In Benchmarks We Trust ... Or Not?

We investigate the identification of idiomatic expressions—a semantically non-compositional subclass of multiword expressions (MWEs)—in running text using large language models (LLMs) without any fine-tuning. Instead, we adopt a prompt-based approach and evaluate a range of prompting strategies, including zero-shot, few-shot, and chain-of-thought variants, across multiple languages, datasets, and model types. Our experiments show that, with well-crafted prompts, LLMs can perform competitively with supervised models trained on annotated data. These findings highlight the potential of prompt-based LLMs as a flexible and effective alternative for idiomatic expression identification.

Easy as PIE? Identifying Multi-Word Expressions with LLMs

Alignment is the critical process of minimizing harmful outputs by teaching large language models (LLMs) to prefer safe, helpful and appropriate responses. While the majority of alignment research and datasets remain overwhelmingly English-centric, ensuring safety across diverse linguistic and cultural contexts requires localized resources. In this paper, we introduce the first Polish preference dataset PLLuM-Align, created entirely through human annotation to reflect Polish language and cultural nuances. The dataset includes response rating, ranking, and multi-turn dialog data. Designed to reflect the linguistic subtleties and cultural norms of Polish, this resource lays the groundwork for more aligned Polish LLMs and contributes to the broader goal of multilingual alignment in underrepresented languages.

PLLuM-Align: Polish Preference Dataset for Large Language Model Alignment

Large language models (LLMs) have achieved remarkable success across diverse domains. However, their potential as effective language teachers—particularly in complex pedagogical scenarios like teaching Chinese as a second language—remains inadequately assessed. To address this gap, we propose the first pedagogical competence benchmark for LLMs, rigorously evaluating their performance against international standards for Chinese language teachers. Our framework spans three core dimensions: (1) basic knowledge, covering 32 subtopics across five major categories (linguistics, Chinese culture, pedagogy, etc.); (2) international teacher examination, based on data collected from international Chinese teacher certification exams; and (3) teaching practice evaluation, where target LLMs summarize knowledge points and design instructional content for a student model, followed by testing the student model to assess the LLM’s ability to distill and teach key concepts. We conduct a comprehensive evaluation of 13 latest multilingual and Chinese LLMs. The results reveal that most existing models struggle to achieve a 60% overall score, highlighting significant room for improvement. This study contributes to the development of AI-assisted language education tools capable of rivaling human teaching excellence.

Can Large Language Models Be Good Language Teachers?

To collaborate effectively with humans, language models must be able to explain their decisions in natural language. We study a specific type of self-explanation: self-generated counterfactual explanations (SCEs), where a model explains its own prediction by modifying the input such that it would have predicted a different outcome. We evaluate whether models can produce SCEs that are valid, achieving the intended outcome, and minimal, modifying the input no more than necessary. We find a trade-off. When simply asked to generate counterfactual explanations, models typically produce SCEs that are valid, but far from minimal, despite this being a well-established property of good counterfactuals. Worryingly, when explicitly instructed to provide minimal counterfactual explanations, the resulting SCEs typically fail to change the models' predictions. No model is able to reliably satisfy both criteria. We examine why models are unable to do this task, arguing they do not engage in self-modelling, the ability to internally predict how they would behave in alternative situations. We argue this is unlikely to be incentivised by standard training techniques and suggest that new learning objectives are required for LLMs to reliably explain themselves counterfactually. Our code is available in the anonymous repository: https://anonymous.4open.science/r/SCEs-3747/README.md.

LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations

Large Language Models (LLMs) have demonstrated remarkable performance across various tasks but remain prone to hallucinations. Detecting hallucinations is essential for safety-critical applications, and recent methods leverage attention map properties to this end, though their effectiveness remains limited. In this work, we investigate the spectral features of attention maps by interpreting them as adjacency matrices of graph structures. We propose the LapEigvals method, which utilises the top-k eigenvalues of the Laplacian matrix derived from the attention maps as an input to hallucination detection probes. Empirical evaluations demonstrate that our approach achieves state-of-the-art hallucination detection performance among attention-based methods. Extensive ablation studies further highlight the robustness and generalisation of LapEigvals, paving the way for future advancements in the hallucination detection domain.

Hallucination Detection in LLMs Using Spectral Features of Attention Maps

We introduce HAMLET, a holistic and automated framework for evaluating the long-context comprehension of large language models (LLMs). HAMLET structures source texts into a three-level key-fact hierarchy at root-, branch-, and leaf-levels, and employs query-focused summarization to evaluate how well models recall and faithfully represent information at each level. To validate the reliability of our fully automated pipeline, we conduct a systematic human study, showing that our automatic evaluation achieves over 90% agreement with expert human judgments, while reducing the cost by up to 25 times. HAMLET reveals that LLMs struggle with fine-grained comprehension, especially at the leaf level, and are sensitive to positional effects like the lost-in-the-middle. Analytical queries pose greater challenges than narrative ones, and consistent performance gaps emerge between open-source and proprietary models, as well as across model scales. A sample of the data is available at https://anonymous.4open.science/r/Eval-1B07/README.md. The full code and datasets will be released.

Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts

Driven by the demand for personalized AI systems, there is growing interest in aligning the behavior of large language models (LLMs) with human traits such as personality. Previous attempts to induce personality in LLMs have shown promising results, but they struggle to capture the continuous and multidimensional nature of human traits. In this work, we propose a novel method for personality modulation in LLMs via model merging. Specifically, we construct personality vectors by subtracting the weights of a pre-trained model from those of the fine-tuned model on a given personality trait. By merging personality vectors, we enable LLMs to exhibit desired personality traits without additional training. Extensive experiments show that personality vectors enable continuous control over trait intensity and support the composition of multiple traits. Furthermore, personality vectors transfer across diverse downstream models, suggesting that they encode generalizable representations of personality.

Personality Vector: Modulating Personality of Large Language Models by Model Merging

In this work, we investigate how multimodal large language models (MLLMs) reconcile memorized world knowledge and visual input. Understanding this balance is essential for building reliable models that can correctly choose between conflicting sources of information. To study this, we introduce Visual CounterFact, a dataset that constructs realistic visual counterfactuals targeting familiar attributes like object color and size. These examples violate learned priors while preserving visual plausibility, enabling precise comparisons between perception and memory. Using this dataset, we find that MLLMs often default to perception, even when prompted to retrieve general knowledge. In these cases, performance on knowledge-based prompts drops significantly, suggesting that models are overly influenced by visual inputs, even when the question targets memorized facts. Through studying the forward-pass, we observe that model predictions initially reflect stored priors, then transition to visually grounded answers in mid-to-late layers. This transition is often unstable, with models flipping between the two sources of information across layers. To control this behavior, we introduce Pixels Versus Priors steering vectors, which allow us to edit model behavior toward preferring either world knowledge priors or visual input. These activation-level interventions produce significant attention shifts towards or away from the image, depending on our steering vector direction. Our findings offer a new framework for interpreting and controlling MLLMs, advancing our ability to understand and control the interaction between memory and perception in multimodal models.

Pixels Versus Priors: Controlling Knowledge Priors in Vision-Language Models through Visual Counterfacts

Robust evaluation is critical for deploying trustworthy retrieval-augmented generation (RAG) systems. However, current LLM-based evaluation frameworks predominantly rely on directly prompting resource-intensive models with complex multi-stage prompts, underutilizing models' reasoning capabilities and introducing significant computational cost. In this paper, we present RAG-Zeval (RAG-Zero Evaluator), a novel end-to-end framework that formulates faithfulness and correctness evaluation as a rule-guided reasoning task. Our approach trains evaluators with reinforcement learning, facilitating compact models to generate comprehensive and sound assessments with detailed explanation in one-pass. We introduce a ranking-based outcome reward mechanism, using preference judgments rather than absolute scores, to address the challenge of obtaining precise pointwise reward signals. To this end, we synthesize the ranking references by generating quality-controlled responses with zero human annotation. Experiments demonstrate RAG-Zeval's superior performance, achieving the strongest correlation with human judgments and outperforming baselines that rely on LLMs with 10-100times more parameters. Our approach also exhibits superior interpretability in response evaluation.

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In Benchmarks We Trust ... Or Not?

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