GenAI4ELab/papercli-papers huggingface dataset:
Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation
Authors: Yanni Xue; Haojie Hao; Jiakai Wang; Qiang Sheng; Renshuai Tao; Yu Liang; Pu Feng; Xianglong Liu
Venue: IJCAI 2024 (main)

Abstract: While neural machine translation (NMT) models achieve success in our daily lives, they show vulnerability to adversarial attacks. Despite being harmful, these attacks also offer benefits for interpreting and enhancing NMT models, thus drawing increased research attention. However, existing studies on adversarial attacks are insufficient in both attacking ability and human imperceptibility due to their sole focus on the scope of language. This paper proposes a novel vision-fused attack (VFA) framework to acquire powerful adversarial text, i.e., more aggressive and stealthy. Regarding the attacking ability, we design the vision-merged solution space enhancement strategy to enlarge the limited semantic solution space, which enables us to search for adversarial candidates with higher attacking ability. For human imperceptibility, we propose the perception-retained adversarial text selection strategy to align the human text-reading mechanism. Thus, the finally selected adversarial text could be more deceptive. Extensive experiments on various models, including large language models (LLMs) like LLaMA and GPT-3.5, strongly support that VFA outperforms the comparisons by large margins (up to 81%/14% improvements on ASR/SSIM).
Learning Label Dependencies for Visual Information Extraction
Authors: Minghong Yao; Liansheng Zhuang; Houqiang Li; Jiuchang Wei
Venue: IJCAI 2024 (main)

Abstract: Visual Information Extraction (VIE), which aims to extract structured information from visually rich document images, has drawn much attention due to its wide applications in document understanding. However, previous methods often treat the VIE task as a sequence labeling problem and ignore the label correlations in the sequence, which may significantly degrade their performance. To address this issue, this paper proposes a novel framework to exploit the potential of label correlations to improve the VIE models' performance. Its key idea is to learn the label dependency of entities, and use it to regularize the label sequence. Specifically, to capture the label dependency of entities, a label transformer is pre-trained to assign a higher likelihood to the label sequence that respects the label patterns of document layouts. During testing stages, an inference transformer is used to predict the label sequence by considering not only the features of each entity but also the likelihood of the label sequence evaluated by the label transformer. Our framework can be combined with existing popular VIE models such as LayoutLM and GeoLayoutLM. Extensive experiments on public datasets have demonstrated the effectiveness of our framework.
Modeling Selective Feature Attention for Lightweight Text Matching
Authors: Jianxiang Zang; Hui Liu
Venue: IJCAI 2024 (main)

Abstract: Representation-based Siamese networks have risen to popularity in lightweight text matching due to their low deployment and inference costs. While word-level attention mechanisms have been implemented within Siamese networks to improve performance, we propose Feature Attention (FA), a novel downstream block designed to enrich the modeling of dependencies among embedding features. Employing "squeeze-and-excitation" techniques, the FA block dynamically adjusts the emphasis on individual features, enabling the network to concentrate more on features that significantly contribute to the final classification. Building upon FA, we introduce a dynamic "selection" mechanism called Selective Feature Attention (SFA), which leverages a stacked BiGRU Inception structure. The SFA block facilitates multi-scale semantic extraction by traversing different stacked BiGRU layers, encouraging the network to selectively concentrate on semantic information and embedding features across varying levels of abstraction. Both the FA and SFA blocks offer a seamless integration capability with various Siamese networks, showcasing a plug-and-play characteristic. Experimental evaluations conducted across diverse text matching baselines and benchmarks underscore the indispensability of modeling feature attention and the superiority of the "selection" mechanism.
InstructEdit: Instruction-Based Knowledge Editing for Large Language Models
Authors: Ningyu Zhang; Bozhong Tian; Siyuan Cheng; Xiaozhuan Liang; Yi Hu; Kouying Xue; Yanjie Gou; Xi Chen; Huajun Chen
Venue: IJCAI 2024 (main)

Abstract: Knowledge editing for large language models can offer an efficient solution to alter a model’s behavior without negatively impacting the overall performance. However, the current approaches encounter issues with limited generalizability across tasks, necessitating one distinct editor for each task, significantly hindering the broader applications. To address this, we take the first step to analyze the multi-task generalization issue in knowledge editing. Specifically, we develop an instruction-based editing technique, termed InstructEdit, which facilitates the editor's adaptation to various task performances simultaneously using simple instructions. With only one unified editor for each LLM, we empirically demonstrate that InstructEdit can improve the editor's control, leading to an average 14.86% increase in Reliability in multi-task editing setting. Furthermore, experiments involving holdout unseen task illustrate that InstructEdit consistently surpass previous strong baselines. To further investigate the underlying mechanisms of instruction-based knowledge editing, we analyze the principal components of the editing gradient directions, which unveils that instructions can help control optimization direction with stronger OOD generalization.
KG-CoT: Chain-of-Thought Prompting of Large Language Models over Knowledge Graphs for Knowledge-Aware Question Answering
Authors: Ruilin Zhao; Feng Zhao; Long Wang; Xianzhi Wang; Guandong Xu
Venue: IJCAI 2024 (main)

Abstract: Large language models (LLMs) encounter challenges such as hallucination and factual errors in knowledge-intensive tasks. One the one hand, LLMs sometimes struggle to generate reliable answers based on the black-box parametric knowledge, due to the lack of responsible knowledge. Moreover, fragmented knowledge facts extracted by knowledge retrievers fail to provide explicit and coherent reasoning paths for improving LLM reasoning. To address these challenges, we propose KG-CoT, a novel knowledge-augmented paradigm that leverages a small-scale step-by-step graph reasoning model to reason over knowledge graphs (KGs) and utilizes a reasoning path generation method to generate chains of reasoning with high confidence for large-scale LLMs. Extensive experiments demonstrate that our KG-CoT significantly improves the performance of LLMs on knowledge-intensive question answering tasks, such as multi-hop, single-hop, and open-domain question answering benchmarks, without fine-tuning LLMs. KG-CoT outperforms the CoT prompting as well as prior retrieval-augmented and knowledge base question answering baselines. Moreover, KG-CoT can reduce the number of API calls and cost and generalize to various LLM backbones in a lightweight plug-and-play manner.
Generate Synthetic Text Approximating the Private Distribution with Differential Privacy
Authors: Wenhao Zhao; Shaoyang Song; Chunlai Zhou
Venue: IJCAI 2024 (main)

Abstract: Due to the potential leakage of sensitive information in text, there is a societal call for feeding privacy-preserving text to model training. Recently, a lot of work showed that using synthetic text with differential privacy, rather than private text, can provide a strong privacy protection. However, achieving higher semantic similarity between synthetic and private text has not been thoroughly investigated. In this paper, we propose an approach that combines the iteratively optimized mindset from genetic algorithms to align the distribution of synthetic text with that of private text. Furthermore, not only does the final synthetic text meet the requirements of privacy protection, but also has a high level of quality. Through comparisons with various baselines on different datasets, we demonstrate that our synthetic text can closely match the utility of private text, while providing privacy protection standards robust enough to resist membership inference attacks from malicious users.
Making LLMs as Fine-Grained Relation Extraction Data Augmentor
Authors: Yifan Zheng; Wenjun Ke; Qi Liu; Yuting Yang; Ruizhuo Zhao; Dacheng Feng; Jianwei Zhang; Zhi Fang
Venue: IJCAI 2024 (main)

Abstract: Relation Extraction (RE) identifies relations between entities in text, typically relying on supervised models that demand abundant high-quality data. Various approaches, including Data Augmentation (DA), have been proposed as promising solutions for addressing low-resource challenges in RE. However, existing DA methods in RE often struggle to ensure consistency and contextual diversity in generated data due to the fine-grained nature of RE. Inspired by the extensive generative capabilities of large language models (LLMs), we introduce a novel framework named ConsistRE, aiming to maintain context consistency in RE. ConsistRE initiates by collecting a substantial corpus from external resources and employing statistical algorithms and semantics to identify keyword hints closely related to relation instances. These keyword hints are subsequently integrated as contextual constraints in sentence generation, ensuring the preservation of relation dependence and diversity with LLMs. Additionally, we implement syntactic dependency selection to enhance the syntactic structure of the generated sentences. Experimental results from the evaluation of SemEval, TACRED, and TACREV datasets unequivocally demonstrate that ConsistRE outperforms other baselines in F1 values by 1.76%, 3.92%, and 2.53%, respectively, particularly when operating under low-resource experimental conditions.
MultifacetEval: Multifaceted Evaluation to Probe LLMs in Mastering Medical Knowledge
Authors: Yuxuan Zhou; Xien Liu; Chen Ning; Ji Wu
Venue: IJCAI 2024 (main)

Abstract: Large language models (LLMs) have excelled across domains, also delivering notable performance on the medical evaluation benchmarks, such as MedQA. However, there still exists a significant gap between the reported performance and the practical effectiveness in real-world medical scenarios. In this paper, we aim to explore the causes of this gap by employing a multifaceted examination schema to systematically probe the actual mastery of medical knowledge by current LLMs. Specifically, we develop a novel evaluation framework MultifacetEval to examine the degree and coverage of LLMs in encoding and mastering medical knowledge at multiple facets (comparison, rectification, discrimination, and verification) concurrently. Based on the MultifacetEval framework, we construct two multifaceted evaluation datasets: MultiDiseK (by producing questions from a clinical disease knowledge base) and MultiMedQA (by rephrasing each question from a medical benchmark MedQA into multifaceted questions). The experimental results on these multifaceted datasets demonstrate that the extent of current LLMs in mastering medical knowledge is far below their performance on existing medical benchmarks, suggesting that they lack depth, precision, and comprehensiveness in mastering medical knowledge. Consequently, current LLMs are not yet ready for application in real-world medical tasks. The codes and datasets are available at https://github.com/THUMLP/MultifacetEval.
Joint Multimodal Aspect Sentiment Analysis with Aspect Enhancement and Syntactic Adaptive Learning
Authors: Linlin Zhu; Heli Sun; Qunshu Gao; Tingzhou Yi; Liang He
Venue: IJCAI 2024 (main)

Abstract: As an important task in sentiment analysis, joint multimodal aspect sentiment analysis (JMASA) has received increasing attention in recent years. However, previous approaches either i) directly fuse multimodal data without fully exploiting the correlation between multimodal input data, or ii) equally utilize the dependencies of words in the text for sentiment analysis, ignoring the differences in the importance of different words. To address these limitations, we propose a joint multimodal sentiment analysis method based on Aspect Enhancement and Syntactic Adaptive Learning (AESAL). Specifically, we construct an aspect enhancement pre-training task to enable the model to fully learn the correlation of aspects between multimodal input data. In order to capture the differences in the importance of different words in the text, we design a syntactic adaptive learning mechanism. First, we construct different syntactic dependency graphs based on the distance between words to learn global and local information in the text. Second, we use a multi-channel adaptive graph convolutional network to maintain the uniqueness of each modality while fusing the correlations between different modalities. Experimental results on benchmark datasets show that our method outperforms state-of-the-art methods.
TFCD: Towards Multi-modal Sarcasm Detection via Training-Free Counterfactual Debiasing
Authors: Zhihong Zhu; Xianwei Zhuang; Yunyan Zhang; Derong Xu; Guimin Hu; Xian Wu; Yefeng Zheng
Venue: IJCAI 2024 (main)

Abstract: Multi-modal sarcasm detection (MSD), which aims to identify whether a given sample with multi-modal information (i.e., text and image) is sarcastic, has garnered widespread attention. Recent approaches focus on designing sophisticated architectures or mechanisms to extract sarcastic cues from entire or local image and text features. Nevertheless, a long-overlooked issue is that current MSD task invariably suffers from unintended dataset biases, especially the statistical label bias and sarcasmless word bias. Concretely, such harmful biases are confounders that may mislead existing models to learn spurious correlations, significantly limiting models' performance. To tackle this issue, this paper proposes a Training-Free Counterfactual Debiasing framework TFCD, which first formulates the causalities among variables in MSD via a tailored causal graph. Then, TFCD extracts biases from the conventionally-trained model by generating counterfactual utterances and contexts and mitigates them using element-wise subtraction. Extensive experiments on two benchmarks demonstrate the effectiveness of the proposed TFCD. Remarkably, TFCD requires neither data balancing nor model modifications, and thus can be seamlessly integrated into diverse state-of-the-art approaches and achieve considerable improvement margins.
Imprecise Probabilities Meet Partial Observability: Game Semantics for Robust POMDPs
Authors: Eline M. Bovy; Marnix Suilen; Sebastian Junges; Nils Jansen
Venue: IJCAI 2024 (main)

Abstract: Partially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this concern by defining imprecise probabilities, referred to as uncertainty sets. While robust MDPs have been studied extensively, work on RPOMDPs is limited and primarily focuses on algorithmic solution methods. We expand the theoretical understanding of RPOMDPs by showing that 1) different assumptions on the uncertainty sets affect optimal policies and values; 2) RPOMDPs have a partially observable stochastic game (POSG) semantic; and 3) the same RPOMDP with different assumptions leads to semantically different POSGs and, thus, different policies and values. These novel semantics for RPOMDPs give access to results for POSGs, studied in game theory; concretely, we show the existence of a Nash equilibrium. Finally, we classify the existing RPOMDP literature using our semantics, clarifying under which uncertainty assumptions these existing works operate.
Solving Long-run Average Reward Robust MDPs via Stochastic Games
Authors: Krishnendu Chatterjee; Ehsan Kafshdar Goharshady; Mehrdad Karrabi; Petr Novotný; Đorđe Žikelić
Venue: IJCAI 2024 (main)

Abstract: Markov decision processes (MDPs) provide a standard framework for sequential decision making under uncertainty. However, MDPs do not take uncertainty in transition probabilities into account. Robust Markov decision processes (RMDPs) address this shortcoming of MDPs by assigning to each transition an uncertainty set rather than a single probability value. In this work, we consider polytopic RMDPs in which all uncertainty sets are polytopes and study the problem of solving long-run average reward polytopic RMDPs. We present a novel perspective on this problem and show that it can be reduced to solving long-run average reward turn-based stochastic games with finite state and action spaces. This reduction allows us to derive several important consequences that were hitherto not known to hold for polytopic RMDPs. First, we derive new computational complexity bounds for solving long-run average reward polytopic RMDPs, showing for the first time that the threshold decision problem for them is in NP and coNP and that they admit a randomized algorithm with sub-exponential expected runtime. Second, we present Robust Polytopic Policy Iteration (RPPI), a novel policy iteration algorithm for solving long-run average reward polytopic RMDPs. Our experimental evaluation shows that RPPI is much more efficient in solving long-run average reward polytopic RMDPs compared to state-of-the-art methods based on value iteration.
Scalable Ultrafast Almost-optimal Euclidean Shortest Paths
Authors: Stefan Funke; Daniel Koch; Claudius Proissl; Axel Schneewind; Armin Weiß; Felix Weitbrecht
Venue: IJCAI 2024 (main)

Abstract: We consider the problem of computing high-quality Euclidean shortest paths amidst obstacles on a large scale. By transferring and adapting speed-up techniques from the road network setting, we are able to compute source target paths for problem instances with several million obstacle vertices within few milliseconds after moderate preprocessing. We show experimentally that for small instances where optimal solutions are easily available on average our computed paths are less than 0.3% longer than the optimum. For large instances a new lower-bounding technique shows that on average our computed paths are less than 2% longer than the optimum paths. We compare our approach with the current state-of-the-art on problem instances derived from the OpenStreetMap project.
Guiding GBFS through Learned Pairwise Rankings
Authors: Mingyu Hao; Felipe Trevizan; Sylvie Thiébaux; Patrick Ferber; Jörg Hoffmann
Venue: IJCAI 2024 (main)

Abstract: We propose a new approach based on ranking to learn to guide Greedy Best-First Search (GBFS). As previous ranking approaches, ours is based on the observation that directly learning a heuristic function is overly restrictive, and that GBFS is capable of efficiently finding good plans for a much more flexible class of total quasi-orders over states. In order to learn an optimal ranking function, we introduce a new ranking framework capable of leveraging any neural network regression model and efficiently handling the training data through batching. Compared with previous ranking approaches for planning, ours does not require complex loss functions and allows training on states outside the optimal plan with minimal overhead. Our experiments on the domains of the latest planning competition learning track show that our approach substantially improves the coverage of the underlying neural network models without degrading plan quality.
Learning Generalized Policies for Fully Observable Non-Deterministic Planning Domains
Authors: Till Hofmann; Hector Geffner
Venue: IJCAI 2024 (main)

Abstract: General policies represent reactive strategies for solving large families of planning problems like the infinite collection of solvable instances from a given domain. Methods for learning such policies from a collection of small training instances have been developed successfully for classical domains. In this work, we extend the formulations and the resulting combinatorial methods for learning general policies over fully observable, non-deterministic (FOND) domains. We also evaluate the resulting approach experimentally over a number of benchmark domains in FOND planning, present the general policies that result in some of these domains, and prove their correctness. The method for learning general policies for FOND planning can actually be seen as an alternative planning FOND planning method that searches for solutions not in the given state space but in an abstract state space defined by features that must be learned as well.
Approximate Dec-POMDP Solving Using Multi-Agent A*
Authors: Wietze Koops; Sebastian Junges; Nils Jansen
Venue: IJCAI 2024 (main)

Abstract: We present an A*-based algorithm to compute policies for finite-horizon Dec-POMDPs. Our goal is to sacrifice optimality in favor of scalability for larger horizons. The main ingredients of our approach are (1) using clustered sliding window memory, (2) pruning the A* search tree, and (3) using novel A* heuristics. Our experiments show competitive performance to the state-of-the-art. Moreover, for multiple benchmarks, we achieve superior performance. In addition, we provide an A* algorithm that finds upper bounds for the optimum, tailored towards problems with long horizons. The main ingredient is a new heuristic that periodically reveals the state, thereby limiting the number of reachable beliefs. Our experiments demonstrate the efficacy and scalability of the approach.
ConstrainedZero: Chance-Constrained POMDP Planning Using Learned Probabilistic Failure Surrogates and Adaptive Safety Constraints
Authors: Robert J. Moss; Arec Jamgochian; Johannes Fischer; Anthony Corso; Mykel J. Kochenderfer
Venue: IJCAI 2024 (main)

Abstract: To plan safely in uncertain environments, agents must balance utility with safety constraints. Safe planning problems can be modeled as a chance-constrained partially observable Markov decision process (CC-POMDP) and solutions often use expensive rollouts or heuristics to estimate the optimal value and action-selection policy. This work introduces the ConstrainedZero policy iteration algorithm that solves CC-POMDPs in belief space by learning neural network approximations of the optimal value and policy with an additional network head that estimates the failure probability given a belief. This failure probability guides safe action selection during online Monte Carlo tree search (MCTS). To avoid overemphasizing search based on the failure estimates, we introduce Δ-MCTS, which uses adaptive conformal inference to update the failure threshold during planning. The approach is tested on a safety-critical POMDP benchmark, an aircraft collision avoidance system, and the sustainability problem of safe CO₂ storage. Results show that by separating safety constraints from the objective we can achieve a target level of safety without optimizing the balance between rewards and costs.