RAUCG: Retrieval-Augmented Unsupervised Counter Narrative Generation for Hate Speech

• URL: http://arxiv.org/abs/2310.05650v1
• Date: Mon, 9 Oct 2023 12:01:26 GMT
• Title: RAUCG: Retrieval-Augmented Unsupervised Counter Narrative Generation for Hate Speech
• Authors: Shuyu Jiang, Wenyi Tang, Xingshu Chen, Rui Tanga, Haizhou Wang and Wenxian Wang
• Abstract summary: The Counter Narrative (CN) is a promising approach to combat online hate speech (HS) without infringing on freedom of speech. Current automatic CN generation methods mainly rely on expert-authored datasets for training. We propose Retrieval-Augmented Unsupervised Counter Narrative Generation (RAUCG) to automatically expand external counter-knowledge and map it into CNs.
• Score: 5.88043557914512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Counter Narrative (CN) is a promising approach to combat online hate speech (HS) without infringing on freedom of speech. In recent years, there has been a growing interest in automatically generating CNs using natural language generation techniques. However, current automatic CN generation methods mainly rely on expert-authored datasets for training, which are time-consuming and labor-intensive to acquire. Furthermore, these methods cannot directly obtain and extend counter-knowledge from external statistics, facts, or examples. To address these limitations, we propose Retrieval-Augmented Unsupervised Counter Narrative Generation (RAUCG) to automatically expand external counter-knowledge and map it into CNs in an unsupervised paradigm. Specifically, we first introduce an SSF retrieval method to retrieve counter-knowledge from the multiple perspectives of stance consistency, semantic overlap rate, and fitness for HS. Then we design an energy-based decoding mechanism by quantizing knowledge injection, countering and fluency constraints into differentiable functions, to enable the model to build mappings from counter-knowledge to CNs without expert-authored CN data. Lastly, we comprehensively evaluate model performance in terms of language quality, toxicity, persuasiveness, relevance, and success rate of countering HS, etc. Experimental results show that RAUCG outperforms strong baselines on all metrics and exhibits stronger generalization capabilities, achieving significant improvements of +2.0% in relevance and +4.5% in success rate of countering metrics. Moreover, RAUCG enabled GPT2 to outperform T0 in all metrics, despite the latter being approximately eight times larger than the former. Warning: This paper may contain offensive or upsetting content!

Related papers

• Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems [11.300387488829035]
  Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses.<n>Existing poisoning attack strategies treat the retrieval and generation stages as disjointed, limiting their effectiveness.<n>Joint-GCG is the first framework to unify gradient-based attacks across both retriever and generator models.
  arXiv  Detail & Related papers  (2025-06-06T15:12:06Z)
• Geometric Retargeting: A Principled, Ultrafast Neural Hand Retargeting Algorithm [50.52643618961044]
  We introduce Geometric Retargeting (GeoRT), an ultrafast, principled hand algorithm for teleoperation. GeoRT converts human finger keypoints to robot hand keypoints at 1KHz, achieving state-of-the-art speed and accuracy.
  arXiv  Detail & Related papers  (2025-03-10T17:10:21Z)
• CTINEXUS: Leveraging Optimized LLM In-Context Learning for Constructing Cybersecurity Knowledge Graphs Under Data Scarcity [49.657358248788945]
  Textual descriptions in cyber threat intelligence (CTI) reports are rich sources of knowledge about cyber threats. Current CTI extraction methods lack flexibility and generalizability, often resulting in inaccurate and incomplete knowledge extraction. We propose CTINexus, a novel framework leveraging optimized in-context learning (ICL) of large language models.
  arXiv  Detail & Related papers  (2024-10-28T14:18:32Z)
• CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks [52.29186466633699]
  Transferable targeted adversarial attacks aim to mislead models into outputting adversary-specified predictions in black-box scenarios. textitsingle-target generative attacks train a generator for each target class to generate highly transferable perturbations. textbfCLIP-guided textbfGenerative textbfNetwork with textbfCross-attention modules (CGNC) to enhance multi-target attacks.
  arXiv  Detail & Related papers  (2024-07-14T12:30:32Z)
• A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation [14.064465097974836]
  This paper proposes a novel approach to evaluate Counter Narrative (CN) generation using a Large Language Model (LLM) as an evaluator. We show that traditional automatic metrics correlate poorly with human judgements and fail to capture the nuanced relationship between generated CNs and human perception.
  arXiv  Detail & Related papers  (2024-06-21T15:11:33Z)
• HGAttack: Transferable Heterogeneous Graph Adversarial Attack [63.35560741500611]
  Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce. This paper introduces HGAttack, the first dedicated gray box evasion attack method for heterogeneous graphs.
  arXiv  Detail & Related papers  (2024-01-18T12:47:13Z)
• Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning [71.8876256714229]
  We propose an entity-based contrastive learning framework for improving the robustness of knowledge-grounded dialogue systems. Our method achieves new state-of-the-art performance in terms of automatic evaluation scores.
  arXiv  Detail & Related papers  (2024-01-09T05:16:52Z)
• Securing Graph Neural Networks in MLaaS: A Comprehensive Realization of Query-based Integrity Verification [68.86863899919358]
  We introduce a groundbreaking approach to protect GNN models in Machine Learning from model-centric attacks. Our approach includes a comprehensive verification schema for GNN's integrity, taking into account both transductive and inductive GNNs. We propose a query-based verification technique, fortified with innovative node fingerprint generation algorithms.
  arXiv  Detail & Related papers  (2023-12-13T03:17:05Z)
• Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization [26.85290529046327]
  We design Graph Neural Networks (GNNs) with attention mechanisms to tackle an important yet challenging nonlinear regression problem: massive network localization. We first review our previous network localization method based on Graph Convolutional Network (GCN) As an extension, we propose a specially designed Attentional GNN (AGNN) model to resolve the sensitive thresholding issue of the GCN-based method.
  arXiv  Detail & Related papers  (2023-11-28T15:05:13Z)
• LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference [19.5669231249754]
  We present LinGCN, a framework designed to reduce multiplication depth and optimize the performance of HE based GCN inference. Remarkably, LinGCN achieves a 14.2x latency speedup relative to CryptoGCN, while preserving an inference accuracy of 75% and notably reducing multiplication depth.
  arXiv  Detail & Related papers  (2023-09-25T17:56:54Z)
• Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience [50.460555688927826]
  We propose the Graph Agent Network (GAgN) to address the vulnerabilities of graph neural networks (GNNs) GAgN is a graph-structured agent network in which each node is designed as an 1-hop-view agent. Agents' limited view prevents malicious messages from propagating globally in GAgN, thereby resisting global-optimization-based secondary attacks.
  arXiv  Detail & Related papers  (2023-06-12T07:27:31Z)
• CTC-based Non-autoregressive Speech Translation [51.37920141751813]
  We investigate the potential of connectionist temporal classification for non-autoregressive speech translation. We develop a model consisting of two encoders that are guided by CTC to predict the source and target texts. Experiments on the MuST-C benchmarks show that our NAST model achieves an average BLEU score of 29.5 with a speed-up of 5.67$times$.
  arXiv  Detail & Related papers  (2023-05-27T03:54:09Z)
• Evaluate Confidence Instead of Perplexity for Zero-shot Commonsense Reasoning [85.1541170468617]
  This paper reconsiders the nature of commonsense reasoning and proposes a novel commonsense reasoning metric, Non-Replacement Confidence (NRC) Our proposed novel method boosts zero-shot performance on two commonsense reasoning benchmark datasets and further seven commonsense question-answering datasets.
  arXiv  Detail & Related papers  (2022-08-23T14:42:14Z)
• Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study [17.338923885534193]
  We present an extensive study on the use of pre-trained language models for the task of automatic Counter Narrative (CN) generation. We first present a comparative study to determine whether there is a particular Language Model (or class of LMs) and a particular decoding mechanism that are the most appropriate to generate CNs. Findings show that autoregressive models combined with decodings are the most promising.
  arXiv  Detail & Related papers  (2022-04-04T12:44:47Z)
• CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks [59.692017490560275]
  Adversarial training has been widely demonstrated to improve model's robustness against adversarial attacks. It remains unclear how the adversarial training could improve the generalization abilities of GNNs in the graph analytics problem. We construct the co-adversarial perturbation (CAP) optimization problem in terms of weights and features, and design the alternating adversarial perturbation algorithm to flatten the weight and feature loss landscapes alternately.
  arXiv  Detail & Related papers  (2021-10-28T02:28:13Z)
• Automated Graph Learning via Population Based Self-Tuning GCN [45.28411311903644]
  Graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks. Traditional GCN models suffer from the issues of overfitting and oversmoothing. Recent techniques like DropEdge could alleviate these issues and thus enable the development of deep GCN.
  arXiv  Detail & Related papers  (2021-07-09T23:05:21Z)
• Time-domain Speech Enhancement with Generative Adversarial Learning [53.74228907273269]
  This paper proposes a new framework called Time-domain Speech Enhancement Generative Adversarial Network (TSEGAN) TSEGAN is an extension of the generative adversarial network (GAN) in time-domain with metric evaluation to mitigate the scaling problem. In addition, we provide a new method based on objective function mapping for the theoretical analysis of the performance of Metric GAN.
  arXiv  Detail & Related papers  (2021-03-30T08:09:49Z)
• Spatio-Temporal Sparsification for General Robust Graph Convolution Networks [16.579675313683627]
  Graph Neural Networks (GNNs) have attracted increasing attention due to its successful applications on various graph-structure data. Recent studies have shown that adversarial attacks are threatening the functionality of GNNs. We propose to defend adversarial attacks on GNN through applying the Spatio-Temporal sparsification (called ST-Sparse) on the GNN hidden node representation.
  arXiv  Detail & Related papers  (2021-03-23T02:03:11Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)
• Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation [22.70189685469752]
  We introduce CGA, a conditional VAE architecture, to control, generate, and augment text. We show the value of the individual model components in an ablation study. We show high quality, diversity and attribute control in the generated sentences through a series of automatic and human assessments.
  arXiv  Detail & Related papers  (2020-04-30T17:31:16Z)
• Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement [102.48582597586233]
  We present a U-Net based attention model, U-Net$_At$, to enhance adversarial speech signals. We conduct experiments on the automatic speech recognition (ASR) task with adversarial audio attacks.
  arXiv  Detail & Related papers  (2020-03-31T02:16:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.