Austria

The introduction of advanced reasoning capabilities have improved the problem-solving performance of large language models, particularly on math and coding benchmarks. However, it remains unclear whether these reasoning models are more or less vulnerable to adversarial prompt attacks than their non-reasoning counterparts. In this work, we present a systematic evaluation of weaknesses in advanced reasoning models compared to similar non-reasoning models across a diverse set of prompt-based attack categories. Using experimental data, we find that on average the reasoning-augmented models are slightly more robust than non-reasoning models (42.51% vs 45.53% attack success rate, lower is better). However, this overall trend masks significant category-specific differences: for certain attack types the reasoning models are substantially more vulnerable (e.g., up to 32 percentage points worse on a tree-of-attacks prompt), while for others they are markedly more robust (e.g., 29.8 points better on cross-site scripting injection). Our findings highlight the nuanced security implications of advanced reasoning in language models and emphasize the importance of stress-testing safety across diverse adversarial techniques.

ACL 2025

Weakest Link in the Chain: Security Vulnerabilities in Advanced Reasoning Models

The AAAI 2025 presidential panel on the future of AI research aims to help all AI stakeholders navigate the recent significant transformations in AI capabilities, as well as AI research methodologies, environments, and communities. It includes 17 chapters, each covering one topic related to AI research, and sketching its history, current trends and open challenges. The study has been conducted by 25 AI researchers and supported by 15 additional contributors and 475 respondents to a community survey.

The Future of AI Research - Webinar Series

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Prompt injection remains a major security risk for large language models. However, the efficacy of existing guardrail models in context-aware settings remains underexplored, as they often rely on static attack benchmarks. Additionally, they have over-defense tendencies. We introduce CAPTURE, a novel context-aware benchmark assessing both attack detection and over-defense tendencies with minimal in-domain examples. Our experiments reveal that current prompt injection guardrail models suffer from high false negatives in adversarial cases and excessive false positives in benign scenarios, highlighting critical limitations. To demonstrate our framework's utility, we train CaptureGuard on our generated data. This new model drastically reduces both false negative and false positive rates on our context-aware datasets while also generalizing effectively to external benchmarks, establishing a path toward more robust and practical prompt injection defenses.

CAPTURE: Context-Aware Prompt Injection Testing and Robustness Enhancement

Safeguarding LLMs requires separating harmful prompts from safe ones. However, current safeguard models often rely on superficial keyword patterns, undermining their robustness. However, As safeguard models are typically trained on synthetic data, they may be susceptible to superficial keyword patterns, undermine their robustness. We frame this reliance as a shortcut learning problem and conduct experiments revealing how existing models depend on specific keywords for classification rather than semantic understanding. Performance evaluations across six safety benchmarks show that models perform well when keyword distributions align but degrade on out-of-distribution prompts. Results from our counterfactual analysis demonstrate that current safeguard models are vulnerable to keyword distribution shifts due to shortcut learning. These findings highlight the importance of addressing shortcut learning to enhance the robustness of safeguard models.

Beyond Words: Multilingual and Multimodal Red Teaming of MLLM Safeguards

Multimodal large language models (MLLMs) are increasingly deployed in real-world applications, yet their safety remains underexplored, particularly in multilingual and visual contexts. In this work, we present a systematic red teaming framework to evaluate MLLM safeguards using adversarial prompts translated into seven languages and delivered via four input modalities: plain text, jailbreak prompt + text, text rendered as an image, and jailbreak prompt + text rendered as an image. We find that rendering prompts as images increases attack success rates and reduces refusal rates, with the effect most pronounced in lower-resource languages such as Slovenian, Czech, and Valencian. Our results suggest that vision-based multilingual attacks expose a persistent gap in current alignment strategies, highlighting the need for robust multilingual and multimodal MLLM safety evaluation and mitigation of these risks. We make our code and data available.

Linguistic Diversity in NLP Security

Despite near-perfect results reported in the literature, the effectiveness of model editing in real-world applications remains unclear. To bridge this gap, we introduce QAEdit, a new benchmark aligned with widely used question answering (QA) datasets, and WILD, a task-agnostic evaluation framework designed to better reflect real-world usage of model editing. Our single editing experiments show that current editing methods perform substantially worse than previously reported (38.5% vs. 96.8%). We demonstrate that it stems from issues in the synthetic evaluation practices of prior work. Among them, the most severe is the use of teacher forcing during testing, which leaks both content and length of the ground truth, leading to overestimated performance. Furthermore, we simulate practical deployment by sequential editing, revealing that current approaches fail drastically with only 1000 edits. This work calls for a shift in model editing research toward rigorous evaluation and the development of robust, scalable methods that can reliably update knowledge in LLMs for real-world use.

The Mirage of Model Editing: Revisiting Evaluation in the Wild

The rapid expansion of digital information and knowledge across structured and unstructured sources has heightened the importance of Information Retrieval (IR). While dense retrieval methods have substantially improved semantic matching for general queries, they consistently underperform on queries with explicit temporal constraints--often those containing numerical expressions and time specifiers such as "in 2015." Existing approaches to Temporal Information Retrieval (TIR) improve temporal reasoning but often suffer from catastrophic forgetting, leading to reduced performance on non-temporal queries. To address this, we propose Time-Specifier Model Merging (TSM), a novel method that enhances temporal retrieval while preserving accuracy on non-temporal queries. TSM trains specialized retrievers for individual time specifiers and merges them into a unified model, enabling precise handling of temporal constraints without compromising non-temporal retrieval. Extensive experiments on both temporal and non-temporal datasets demonstrate that TSM significantly improves performance on temporally constrained queries while maintaining strong results on non-temporal queries, consistently outperforming other training methods. Our code is available at https://github.com/seungyoonee/TSM.

Temporal Information Retrieval via Time-Specifier Model Merging

A major bottleneck in exam construction involves designing test items (i.e., questions) that accurately reflect key content from domain-aligned curricular materials. For instance, during formative assessments in vocational education and training (VET), exam designers must generate updated test items that assess student learning progress while covering the full breadth of topics in the curriculum. Large language models (LLMs) can partially support this process, but effective use requires careful prompting and task-specific understanding. We propose a new key point extraction method for retrieval-augmented item generation that enhances the process of generating test items with LLMs. We exhaustively evaluated our method using a TREC-RAG approach, finding that prompting LLMs with key content rather than directly using full curricular text passages significantly improves item quality regarding key information coverage by 8%. To demonstrate these findings, we release EdTec-ItemGen, a retrieval-augmented item generation demo tool to support item generation in education.

EdTec-ItemGen: Enhancing Retrieval-Augmented Item Generation Through Key Point Extraction

Large language models (LLMs) often suffer from hallucinations, posing significant challenges for real-world applications. Confidence calibration, as an effective indicator of hallucination, is thus essential to enhance the trustworthiness of LLMs. Prior work mainly focuses on short-form tasks using a single response-level score (macro calibration), which is insufficient for long-form outputs that may contain both accurate and inaccurate claims. In this work, we systematically study atomic calibration, which evaluates factuality calibration at a fine-grained level by decomposing long responses into atomic claims. We further categorize existing confidence elicitation methods into discriminative and generative types, and propose two new confidence fusion strategies to improve calibration. Our experiments demonstrate that LLMs exhibit poorer calibration at the atomic level during long-form generation. More importantly, atomic calibration uncovers insightful patterns regarding the alignment of confidence methods and the changes of confidence throughout generation. This sheds light on future research directions for confidence estimation in long-form generation.

Atomic Calibration of LLMs in Long-Form Generations

Large language models (LLMs) have achieved great success, but their occasional content fabrication, or hallucination, limits their practical application. Hallucination arises because LLMs struggle to admit ignorance due to inadequate training on knowledge boundaries. We call it a limitation of LLMs that they can not accurately express their knowledge boundary, answering questions they know while admitting ignorance to questions they do not know. In this paper, we aim to teach LLMs to recognize and express their knowledge boundary, so they can reduce hallucinations caused by fabricating when they do not know. We propose CoKE, which first probes LLMs' knowledge boundary via internal confidence given a set of questions, and then leverages the probing results to elicit the expression of the knowledge boundary. Extensive experiments show CoKE helps LLMs express knowledge boundaries, answering known questions while declining unknown ones, significantly improving in-domain and out-of-domain performance.

Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals

Despite Large Language Models' advances, document-grounded generation still suffers from factual errors. Current evaluations oversimplify error analysis by applying binary judgements, while costly human-annotated datasets contain under-representative error distributions. To address these challenges, we propose a novel framework named SIS-Fact (Systematic, Interpretable and Scalable Factuality Evaluation), which integrates systematic error typologies, synthetic data generation pipelines, and high-quality interpretable annotations for comprehensive factuality evaluation. Specifically, we first develop ten diverse methods to synthesize six error types in grounded generation, including both intrinsic and extrinsic errors. In this way, we develop SIS-Fact Dataset, a high-quality document-grounded factuality evaluation dataset characterized by challenging errors and interpretable error analysis. Based on SIS-Fact Dataset, we introduce SIS-Fact Evaluator, an advanced factuality evaluation model capable of fine-grained analysis and correction. Our extensive experiments show that SIS-Fact Evaluator achieves SOTA performance in SIS-Fact Dataset while maintaining strong generalization across existing multiple factuality benchmarks.

SIS-Fact: Towards Systematic, Interpretable and Scalable Factuality Evaluation for LLM

Large language models (LLMs) encode vast amounts of knowledge during pre-training (parametric knowledge, or PK) and can further be enhanced by incorporating contextual knowledge (CK). Can LLMs effectively integrate their internal PK with external CK to solve complex problems? In this paper, we investigate the dynamic interaction between PK and CK, categorizing their relationships into four types: Supportive, Complementary, Conflicting, and Irrelevant. To support this investigation, we introduce ECHOQA, a benchmark spanning scientific, factual, and commonsense knowledge. Our results show that LLMs tend to suppress their PK when contextual information is available, even when it is complementary or irrelevant. While tailored instructions can encourage LLMs to rely more on their PK, they still struggle to fully leverage it. These findings reveal a key vulnerability in LLMs, raising concerns about their reliability in knowledge-intensive tasks. We will release our code and dataset to facilitate future research.

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