United States

Large Language Models (LLMs) are susceptible to malicious influence by cyber attackers through intrusions such as adversarial, backdoor, and embedding inversion attacks. 
In response, the burgeoning field of LLM Security aims to study and defend against such threats. 
Thus far, the majority of works in this area have focused on monolingual English models, however, emerging research suggests that multilingual LLMs may be more vulnerable to various attacks than their monolingual counterparts. 
While previous work has investigated embedding inversion over a small subset of European languages, it is challenging to extrapolate these findings to languages from different linguistic families and with differing scripts. 
To this end, we explore the security of multilingual LLMs in the context of embedding inversion attacks and investigate cross-lingual and cross-script 
inversion across 20 languages, spanning over 8 language families and 12 scripts.
Our findings indicate that languages written in Arabic script and Cyrillic script are particularly vulnerable to embedding inversion, as are languages within the Indo-Aryan language family. 
We further observe that inversion models tend to suffer from language confusion, sometimes greatly reducing the efficacy of an attack. 
Accordingly, we systematically explore this bottleneck for inversion models, uncovering predictable patterns which could be leveraged by attackers. 
Ultimately, this study aims to further the field&#39;s understanding of the outstanding security vulnerabilities facing multilingual LLMs and raise awareness for the languages most at risk of negative impact from these attacks.

AAAI 2025

Against All Odds: Overcoming Typology, Script, and Language Confusion in Multilingual Embedding Inversion Attacks

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

You are required to register for this event. **Please register [here](https://2024.aclweb.org/registration). **

If you have already registered, please check your inbox for an email from Underline granting you access to ACL 2024 content.

Please register!

ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Large Language Models (LLMs) are susceptible to malicious influence by cyber attackers through intrusions such as adversarial, backdoor, and embedding inversion attacks. 
In response, the burgeoning field of LLM Security aims to study and defend against such threats. 
Thus far, the majority of works in this area have focused on monolingual English models, however, emerging research suggests that multilingual LLMs may be more vulnerable to various attacks than their monolingual counterparts. 
While previous work has investigated embedding inversion over a small subset of European languages, it is challenging to extrapolate these findings to languages from different linguistic families and with differing scripts. 
To this end, we explore the security of multilingual LLMs in the context of embedding inversion attacks and investigate cross-lingual and cross-script 
inversion across 20 languages, spanning over 8 language families and 12 scripts.
Our findings indicate that languages written in Arabic script and Cyrillic script are particularly vulnerable to embedding inversion, as are languages within the Indo-Aryan language family. 
We further observe that inversion models tend to suffer from language confusion, sometimes greatly reducing the efficacy of an attack. 
Accordingly, we systematically explore this bottleneck for inversion models, uncovering predictable patterns which could be leveraged by attackers. 
Ultimately, this study aims to further the field's understanding of the outstanding security vulnerabilities facing multilingual LLMs and raise awareness for the languages most at risk of negative impact from these attacks.

poster

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 ability to estimate temporal relationships is critical for both animals and artificial agents. Cognitive science and neuroscience provide remarkable insights into behavioral and neural aspects of temporal credit assignment. In particular, scale-invariance of learning dynamics observed in behavior and supported by neural data is one of the key principles that governs animal perception: if the temporal relationships in the environment rescale, the number of trials required for learning will remain constant. Here we integrate a computational neuroscience model of scale-invariant memory into deep reinforcement learning (RL) agents. We first provide a theoretical analysis and then demonstrate through experiments that such agents can learn robustly across a wide range of temporal scales, unlike agents built with commonly used recurrent memory architectures such as LSTM. This result illustrates that building computational principles from neuroscience and cognitive science into deep neural networks can result in systems that are flexible learners, similar to humans.

Deep Reinforcement Learning with Time-Scale Invariant Memory

In this paper, we draw an analogy between processing natural languages and processing multivariate event streams from vehicles in order to predict when and what error pattern is most likely to occur in the future for a given car. Our approach leverages the temporal dynamics and contextual relationships of our event data from a fleet of cars. Event data is composed of discrete values of error codes as well as continuous values such as time and mileage. Modelled by two causal transformers, we can anticipate critical vehicle failures and malfunctions before they happen. Thus, we introduce CarFormer, a transformer model trained via a new self-supervised learning strategy, and Epredictor, an autoregressive transformer decoder model capable of predicting when and what error pattern will most likely occur after some error code apparition. Despite the challenges of high cardinality of event types, their unbalanced frequency of appearance and a limited labeled data, our experimental results demonstrate the excellent predictive ability of our novel model. Specifically, with on average sequences of 160 error codes, our model is able with only half of the error codes to achieve $80$% F1 score for predicting what error pattern will occur and achieves an average absolute error of $58.4 \pm 13.2$h when forecasting the time of occurrence,e, thus enabling confident predictive maintenance and enhancing vehicle safety.

Harnessing Event Sensory Data for Error Pattern Prediction in Vehicles: A Language Model Approach

As Large Language Models (LLMs) excel in traditional NLP tasks, their application to complex, strategic games like poker poses a new challenge. Poker, an incomplete information game, demands a multitude of skills such as mathematics, reasoning, planning, and understanding of game theory. We evaluate prominent models such as GPT, Llama and Gemma series models, finding that all state-of-the-art LLMs underperform in playing optimal poker. To enhance LLM’s poker-playing capabilities, we formulated strategy learning in a teacher-student framework and performed knowledge distillation from Game Theory Optimal (GTO) strategy solvers. Through aggregating a novel training dataset comprising decisions that represent an unexploitable Nash Equilibrium strategy and supervised fine-tuning (SFT), we distill the GTO solver's output into LLMs, resulting in substantial improvements in model performance. Meanwhile, we introduce PokerBench - a benchmark for evaluating the poker-playing abilities of LLMs. PokerBench consists of a comprehensive compilation of 2,000 essential scenarios in poker. We validate PokerBench by demonstrating that higher test accuracy on PokerBench correlates well with higher expected value of actions taken, leading to a higher win rate in actual gameplay. PokerBench thus presents a unique benchmark for an efficient and reliable evaluation of the poker-playing ability of LLMs as well as a comprehensive benchmark to study the progress of LLMs in complex game-playing scenarios with incomplete information.

PokerBench: Training Large Language Models to Become Professional Poker Players

This paper challenges the prevailing view that convolutional neural network (CNN) filters become increasingly specialized in deeper layers. Motivated by recent observations of clusterable repeating patterns in depthwise separable CNNs (DS-CNNs) trained on ImageNet, we extend this investigation across various domains and datasets. Our analysis of DS-CNNs reveals that deep filters maintain generality, contradicting the expected transition to class-specific features. We demonstrate the generalizability of these filters through transfer learning experiments, showing that frozen filters from models trained on different datasets perform well and can be further improved when sourced from larger, better-performing models. Our findings indicate that spatial features learned by depthwise separable convolutions remain generic across all layers, domains, and architectures. This research provides new insights into the nature of generalization in neural networks, particularly in DS-CNNs, and has significant implications for transfer learning and model design.

The Master Key Filters Hypothesis: Deep Filters Are General

Neural combinatorial optimization (NCO) has gained significant attention due to the potential of deep learning to efficiently solve combinatorial optimization problems. NCO has been widely applied to job shop scheduling problems (JSPs) with the current focus predominantly on deterministic problems. In this paper, we propose a novel attention-based scenario processing module (SPM) to extend NCO methods for solving stochastic JSPs. Our approach explicitly incorporates stochastic information by an attention mechanism that captures the embedding of sampled scenarios (i.e., an approximation of stochasticity). Fed with the embedding, the base neural network is intervened by the attended scenarios, which accordingly learns an effective policy under stochasticity. We also propose a training paradigm that works harmoniously with either the expected makespan or Value-at-Risk objective. Results demonstrate that our approach outperforms existing learning and non-learning methods for the flexible JSP problem with stochastic processing times on a variety of instances. In addition, our approach holds significant generalizability to varied numbers of scenarios and disparate distributions.

Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems

We propose SoundBrush, a model that uses sound as a brush to edit and manipulate visual scenes. We extend the generative capabilities of the Latent Diffusion Model (LDM) to incorporate audio information for editing visual scenes. Inspired by existing image-editing works, we frame this task as a supervised learning problem and leverage various off-the-shelf models to construct a sound-paired visual scene dataset for training. This richly generated dataset enables SoundBrush to learn to map audio features into the textual space of the LDM, allowing for visual scene editing guided by diverse in-the-wild sound. Unlike existing methods, SoundBrush can accurately manipulate the overall scenery or even insert sounding objects to best match the audio inputs while preserving the original content. Furthermore, by integrating with novel view synthesis techniques, our framework can be extended to edit 3D scenes, facilitating sound-driven 3D scene manipulation.

SoundBrush: Sound as a Brush for Visual Scene Editing

Deep learning models with large-scale backbones have been increasingly adopted to tackle complex visual question answering (VQA) problems in real settings. While providing powerful learning capacities to handle the high-dimensional and multimodal VQA data, these models tend to suffer from the memorization effect leading to overconfident predictions. This can significantly limit their applicability in critical domains (e.g., medicine, cyber-security, and public safety), where confidently wrong predictions may lead to severe consequences. In this work, we propose to perform novel low-rank network factorization, resulting in much better-calibrated networks. These low-rank factorized networks are then aggregated into an ensemble guided by a generalized focal loss to further improve the overall performance and calibration. The overall framework, referred to as the Generalized focal Loss Ensemble of low-rank Networks (GLEN), is an important step toward developing well-calibrated VQA models. We demonstrate the effectiveness of our low-rank factorization-based ensemble technique towards calibrating VQA models, theoretically demonstrate that the generalized focal loss provides a more balanced bias-variance trade-off, which guarantees to lower the confidence of the incorrect predictions. Extensive experimentation conducted on benchmark datasets and comparison on various VQA models shows that GLEN leads to much better calibration over both in-distribution and out-of-distribution data without sacrificing the VQA accuracy.

GLEN: Generalized Focal Loss Ensemble of Low-Rank Networks for Calibrated Visual Question Answering

In this paper, we propose a new benchmark called "Archaeological Piece Grouping." In the field of archaeology, it is common for broken archaeological pieces, such as artifact fragments, to be mixed. Archaeologists often spend significant time distinguishing these pieces and categorizing them into different groups. Our benchmark introduces a novel, comprehensive dataset named ArcPie, along with new evaluation metrics for this task. Additionally, we propose a new framework called "3D Probabilistic Graph Search" (3DPGS) to address the problem of grouping mixed archaeological pieces. This framework includes a relation network designed to learn the relationships among all the input 3D pieces. Utilizing the relationships learned, our framework generates a probabilistic matching graph that describes the affinity of any two pieces. We also introduce a novel search algorithm to identify groups according to this matrix. Our framework significantly outperforms other baselines. We commit to releasing our code and dataset if our paper is accepted.

3DPGS: 3D Probabilistic Graph Search for Archaeological Piece Grouping

Wearable Human Action Recognition (wHAR) uses motion sensor data to identify human movements, which is essential for mobile and wearable devices. However, traditional wHAR systems are only trained on a limited set of activities. Hence, they fail to generalize to diverse human motions, prompting Zero-Shot Learning (ZSL). Existing ZSL methods for wHAR focus solely on augmenting labels, such as representing them as attribute matrices, images, videos, or text. We propose ZeroHAR that enhances ZSL by not just focusing on activity labels, but by augmenting motion data with sensor context features. Our approach incorporates information about the sensor type, the Cartesian axis of the data, and the sensor's body position, providing the model with crucial spatial and biomechanical insights. This helps the model generalize better to new actions. First, we train the model by aligning the latent space of the motion time-series with its corresponding sensor context, while distancing it from unrelated sensor contexts. Finally, we train the model using the target activity descriptions. We tested our method against eight baselines on five benchmark HAR datasets with various sensors, placements, and activities. Our model shows exceptional generalizability across 18 motion time series classification benchmark datasets, outperforming the best baselines by 262% in the zero-shot setting.

ZeroHAR: Sensor Context Augments Zero-Shot Wearable Action Recognition

Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF systems where agents need to replan fast. Methods that address this typically employ a ``windowed" approach and only try to find collision free paths for a small windowed timestep horizon. This adaptation comes at the cost of incompleteness; all current windowed approaches can become stuck in deadlock or livelock. Our main contribution is to introduce our framework, WinC-MAPF, for Windowed MAPF that enables completeness. Our framework uses heuristic update insights from single-agent real-time heuristic search algorithms as well as agent independence ideas from MAPF algorithms. We also develop Single-Step CBS (SS-CBS), an instantiation of this framework using a novel modification to CBS.
We show how SS-CBS, which only plans a single step and updates heuristics, can effectively solve tough scenarios where existing windowed approaches fail.

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Deep Reinforcement Learning with Time-Scale Invariant Memory

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Text Embedding Inversion Security for Multilingual Language Models