United States

We introduce LLM Stinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional methods, which require complex prompt engineering or white-box access, LLM Stinger uses a reinforcement learning (RL) loop to fine-tune an attacker LLM, generating new suffixes based on existing attacks for harmful questions from the HarmBench benchmark. Our method significantly outperforms existing red-teaming approaches (we compared against 15 of the latest methods), achieving a +57.2% improvement in Attack Success Rate (ASR) on LLaMA2-7B-chat and a +50.3% ASR increase on Claude 2, both models known for their extensive safety measures. Additionally, we achieved a 94.97% ASR on GPT-3.5 and 99.4% on Gemma-2B-it, demonstrating the robustness and adaptability of LLM Stinger across open and closed-source models.

AAAI 2025

LLM Stinger: Jailbreaking LLMs Using RL Fine-Tuned LLMs (Student Abstract)

### 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. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

[![](https://assets.underline.io/markdown_image/1/image/b087f8a4dc5816d6e1a6514e59c59ac3.png)](https://drive.google.com/file/d/1T96GzPqObXrMln2BMByCSXTSizjTg69P/view?usp=sharing)

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

To access NAACL 2025 event page you are required to register. Please follow [**this link**](https://2025.naacl.org/registration/registration/) to register. Access will depend on your registration type.

Please register!

NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



The quality of interactions between parents and children is a critical factor in child development. Recent years have seen programs to improve parenting behaviors through evidence-based approaches, such as attachment-based interventions. A vital element of these programs is to assess the quality of parenting behaviors via video recordings of parent-child interactions, which is often time-intensive. In our previous work, we explored machine learning models to predict expert ratings of parenting behaviors from video recordings of semi-structured parent-child play. However, the large set of low-level multimodal features struggled to provide explainable insights, which created barriers to communicating with domain experts and improving the models further. In this work, we developed a machine learning pipeline that combines sparse multiple canonical correlation analysis with causal discovery techniques to uncover explainable causal relationships between nine categories of behavioral features and the quality ratings of parent-child interactions. This approach offers valuable insights into the otherwise black-box models and contributes to the growing body of work on transparent and trustworthy machine learning models of parenting behaviors.

Causal Explanation of Quality of Parent-Child Interactions with Multimodal Behavioral Features (Student Abstract)

Knowledge bases traditionally require manual optimization to ensure reasonable performance when answering queries. We build on previous neurosymbolic approaches by improving the training of an embedding model for logical statements that maximizes similarity between unifying atoms and minimizes similarity of non-unifying atoms. In particular, we evaluate different approaches to training this model.

An Evaluation of Approaches to Train Embeddings for Logical Inference (Student Abstract)

Phone scams pose a significant threat to individuals and communities, causing substantial financial losses and emotional distress. Despite ongoing efforts to combat these scams, scammers continue to adapt and refine their tactics, making it imperative to explore innovative countermeasures. This research explores the potential of large language models (LLMs) to provide detection of fraudulent phone calls. By analyzing the conversational dynamics between scammers and victims, LLM-based detectors can identify potential scams as they occur, offering immediate protection to users. While such approaches demonstrate promising results, we also acknowledge the challenges of biased datasets, relatively low recall, and hallucinations that must be addressed for further advancement in this field.

Combating Phone Scams with LLM-based Detection: Where Do We Stand? (Student Abstract)

This paper makes a novel combination of a recently proposed bias audit framework and a recently proposed jailbreaking technique for Llama3. On an audit comprising several disadvantaged groups, our experiments reveal that a jailbroken Llama3 exhibits worrisome antisemitism, racism, misogyny, and homophobia (to list a few) much akin to a broad suite of LLMs that were susceptible to similar biases.

All You Need Is S P A C E: When Jailbreaking Meets Bias Audit and Reveals What Lies Beneath the Guardrails (Student Abstract)

Semantic segmentation of marine environments is essential for autonomous navigation of unmanned surface vessels (USVs) as well as the detection of environmental hazards such as oil spills. To tackle the challenges of accurate environmental perception, we propose a lightweight semantic segmentation network, LAqua (Laplacians for Aquatic Segmentation), which leverages Laplacian pyramids to enhance edge detection in marine imagery. Our method drastically reduces computational requirements while maintaining high accuracy in generating semantic masks for marine environments. We evaluate LAqua on two distinct datasets: one focused on detecting oil spills in port environments and another on environmental segmentation for USVs. Results show that LAqua not only performs well across varied marine settings but also achieves comparable or superior segmentation accuracy with far fewer parameters than other models. This efficiency highlights LAqua's potential for applications in real-time detection for marine environments.

LAqua: Laplacian Pyramids for Aquatic Segmentation (Student Abstract)

The prevalence of multi-modal content on social media complicates automated moderation strategies. This calls for an enhancement in multi-modal classification and a deeper understanding of understated meanings in images and memes. Although previous efforts have aimed at improving model performance through fine-tuning, few have explored an end-to-end optimization pipeline that accounts for modalities, prompting, labelling, and fine-tuning. In this study, we propose an end-to-end conceptual framework for model opti- mization in complex tasks. Experiments support the efficacy of this traditional yet novel framework, achieving the highest accuracy and AUROC. Ablation experiments demonstrate that isolated optimisations are not ineffective on their own.

Hateful Meme Detection through Context-Sensitive Prompting and Fine-Grained Labeling (Student Abstract)

Recently, researchers have focused on methods that not only distill knowledge from a Graph Neural Network (GNN) into a Multi-Layer Perceptron (MLP) but also leverage multiple teacher GNNs. However, existing methods assign a single attention weight to each teacher GNN. We propose a NodeAware Attention Mechanism (NAAM) that flexibly adjusts the attention weight for each node to leverage multiple GNNs fully. Experimental results show that NAAM outperforms existing GNN-to-MLP methods. our source code is available at: https://github.com/NakayamaItsuki/NAAM.

NAAM: Node-Aware Attention Mechanism for Distilling GNNs-to-MLP (Student Abstract)

Existing Multimodal Large Language Models (MLLMs) and Visual Language Pretrained Models (VLPMs) have shown remarkable performances in general Visual Question Answering (VQA). However, these models struggle with VQA questions that require external commonsense knowledge due to the challenges in generating high-quality prompts and the high computational costs of fine-tuning. In this work, we propose a novel graph-based multimodal commonsense knowledge distillation framework that constructs a unified relational graph over commonsense knowledge, visual objects and questions through a Graph Convolutional Network (GCN) following a teacher-student environment. This proposed framework is flexible with any type of teacher and student models without further fine-tuning, and has achieved competitive performances on the ScienceQA dataset. The code is in https://github.com/adlnlp/MCKDVQA.

Multimodal Commonsense Knowledge Distillation for Visual Question Answering (Student Abstract)

Conformal prediction (CP) has gained prominence as a popular technique for uncertainty quantification in deep neural networks (DNNs), providing statistically rigorous uncertainty sets. However, existing CP methods fail to clarify the origins of predictive uncertainties. While neuron-level interpretability has been effective in revealing the internal mechanisms of DNNs, explaining CP at the neuron level remains unexplored. Nonetheless, generating neuron explanations for CP is challenging due to the discrete and non-differentiable characteristics of CP, and the labor-intensive process of semantic annotation. To address these limitations, this paper proposes a novel neuron explanation approach for CP by identifying neurons crucial for understanding predictive uncertainties and automatically generating semantic explanations. The effectiveness of the proposed method is validated through both qualitative and quantitative experiments.

Neuron Explanations for Conformal Prediction (Student Abstract)

The task of 3D object detection is crucial for various applications that rely on identifying objects in three-dimensional space using inputs like LiDAR point clouds and images. However, LiDAR-based detection faces challenges due to the sparsity of point clouds, especially at greater distances. To address this, depth completion models have been used to generate virtual points from RGB images, but they struggle with real-time applications due to high computational costs. Our work eliminates the depth completion process, significantly improving processing speed while minimizing performance degradation. Consequently, our method has achieved an optimal balance between speed and accuracy on the KITTI leaderboard.

Downloads

Next from AAAI 2025

Causal Explanation of Quality of Parent-Child Interactions with Multimodal Behavioral Features (Student Abstract)

Similar lecture

Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AAAI 2025

Causal Explanation of Quality of Parent-Child Interactions with Multimodal Behavioral Features (Student Abstract)

Similar lecture

Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities

Downloads