pwc-archive/papers-with-abstracts huggingface dataset:
User Consented Federated Recommender System Against Personalized Attribute Inference Attack
Authors: Qi Hu, Yangqiu Song
Date: 2023-12-23 00:00:00
Tasks: Attribute, Federated Learning, Inference Attack, Recommendation Systems

Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared recommendation model on local devices and prevent raw data transmissions and collections. However, the recommendation model learned by a common FedRec may still be vulnerable to private information leakage risks, particularly attribute inference attacks, which means that the attacker can easily infer users' personal attributes from the learned model. Additionally, traditional FedRecs seldom consider the diverse privacy preference of users, leading to difficulties in balancing the recommendation utility and privacy preservation. Consequently, FedRecs may suffer from unnecessary recommendation performance loss due to over-protection and private information leakage simultaneously. In this work, we propose a novel user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users by paying a minimum recommendation accuracy price. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent. Experiments conducted on different real-world datasets demonstrate that our framework is more efficient and flexible compared to baselines.
Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns
Authors: Xin Liu, Zheng Li, Yifan Gao, Jingfeng Yang, Tianyu Cao, Zhengyang Wang, Bing Yin, Yangqiu Song
Date: 2023-12-23 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Attribute, Session-Based Recommendations

The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history. However, constructing global or local transition graphs to supplement session data can lead to noisy correlations and user intent vanishing. In this work, we propose the Frequent Attribute Pattern Augmented Transformer (FAPAT) that characterizes user intents by building attribute transition graphs and matching attribute patterns. Specifically, the frequent and compact attribute patterns are served as memory to augment session representations, followed by a gate and a transformer block to fuse the whole session information. Through extensive experiments on two public benchmarks and 100 million industrial data in three domains, we demonstrate that FAPAT consistently outperforms state-of-the-art methods by an average of 4.5% across various evaluation metrics (Hits, NDCG, MRR). Besides evaluating the next-item prediction, we estimate the models' capabilities to capture user intents via predicting items' attributes and period-item recommendations.
Learning to Generate Text in Arbitrary Writing Styles
Authors: Aleem Khan, Andrew Wang, Sophia Hager, Nicholas Andrews
Date: 2023-12-28 00:00:00
Tasks: Language Modeling, Language Modelling, Style Transfer, Text Generation

Prior work in style-controlled text generation has focused on tasks such as emulating the style of prolific literary authors, producing formal or informal text, and mitigating toxicity of generated text. Plentiful demonstrations of these styles are available, and as a result modern language models are often able to emulate them, either via prompting or discriminative control. However, in applications such as writing assistants, it is desirable for language models to produce text in an author-specific style on the basis of a potentially small writing sample. For example, someone writing in a particular dialect may prefer writing suggestions that retain the same dialect. We find that instruction-tuned language models can struggle to reproduce author-specific style demonstrated in a prompt. Instead, we propose to guide a language model to generate text in a target style using contrastively-trained representations that capture stylometric features. Our approach (StyleMC) combines an author-adapted language model with sequence-level inference to improve stylistic consistency, and is found to be effective in a variety of conditions, including unconditional generation and style transfer. Additionally, we find that the proposed approach can serve as an effective anonymization method, by editing a document to mask authorship while preserving the original meaning
MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation
Authors: Zhongshen Zeng, Pengguang Chen, Shu Liu, Haiyun Jiang, Jiaya Jia
Date: 2023-12-28 00:00:00
Tasks: GSM8K, Language Model Evaluation, Language Modeling, Language Modelling, Large Language Model, Math, Question Answering

In this work, we introduce a novel evaluation paradigm for Large Language Models (LLMs) that compels them to transition from a traditional question-answering role, akin to a student, to a solution-scoring role, akin to a teacher. This paradigm, focusing on "reasoning about reasoning," hence termed meta-reasoning, shifts the emphasis from result-oriented assessments, which often neglect the reasoning process, to a more comprehensive evaluation that effectively distinguishes between the cognitive capabilities of different models. By applying this paradigm in the GSM8K dataset, we have developed the MR-GSM8K benchmark. Our extensive analysis includes several state-of-the-art models from both open-source and commercial domains, uncovering fundamental deficiencies in their training and evaluation methodologies. Notably, while models like Deepseek-v2 and Claude3-Sonnet closely competed with GPT-4 in GSM8K, their performance disparities expanded dramatically in MR-GSM8K, with differences widening to over 20 absolute points, underscoring the significant challenge posed by our meta-reasoning approach.
Improving In-context Learning via Bidirectional Alignment
Authors: Chengwei Qin, Wenhan Xia, Fangkai Jiao, Chen Chen, Yuchen Hu, Bosheng Ding, Shafiq Joty
Date: 2023-12-28 00:00:00
Tasks: In-Context Learning

Large language models (LLMs) have shown impressive few-shot generalization on many tasks via in-context learning (ICL). Despite their success in showing such emergent abilities, the scale and complexity of larger models also lead to unprecedentedly high computational demands and deployment challenges. In reaction, researchers explore transferring the powerful capabilities of larger models to more efficient and compact models by typically aligning the output of smaller (student) models with that of larger (teacher) models. Existing methods either train student models on the generated outputs of teacher models or imitate their token-level probability distributions. However, these distillation methods pay little to no attention to the input, which also plays a crucial role in ICL. Based on the finding that the performance of ICL is highly sensitive to the selection of demonstration examples, we propose Bidirectional Alignment (BiAlign) to fully leverage the models' preferences for ICL examples to improve the ICL abilities of student models. Specifically, we introduce the alignment of input preferences between student and teacher models by incorporating a novel ranking loss, in addition to aligning the token-level output distribution. With extensive experiments and analysis, we demonstrate that BiAlign can consistently outperform existing baselines on a variety of tasks involving language understanding, reasoning, and coding.
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
Authors: Liang Zhao, Xiachong Feng, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin, Ting Liu
Date: 2023-12-28 00:00:00
Tasks: Position, Survey

Built upon the Transformer, large language models (LLMs) have captured worldwide attention due to their remarkable abilities. Nevertheless, all Transformer-based models including LLMs suffer from a preset length limit and can hardly generalize from short training sequences to longer inference ones, namely, they cannot perform length extrapolation to handle long sequences, which severely hinders their application in scenarios demanding long input sequences such as legal or scientific documents. Thus, numerous methods have emerged to enhance the length extrapolation of Transformers. Despite the great research efforts, a systematic survey is still lacking. To fill this gap, we delve into these advances in a unified notation from the perspective of positional encoding (PE), as it has been considered the primary factor on length extrapolation. Specifically, we begin with extrapolatable PEs that have dominated this research field. Then, we dive into extrapolation methods based on them, covering position interpolation and randomized position methods. Finally, several challenges and future directions in this area are highlighted. Through this survey, we aim to enable the reader to gain a deep understanding of existing methods and provide stimuli for future research.
Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine
Authors: Jonas Rieger, Kostiantyn Yanchenko, Mattes Ruckdeschel, Gerret von Nordheim, Katharina Kleinen-von Königslöw, Gregor Wiedemann
Date: 2023-12-28 00:00:00
Tasks: Few-Shot Learning, Task 2

Pre-trained language models (PLM) based on transformer neural networks developed in the field of natural language processing (NLP) offer great opportunities to improve automatic content analysis in communication science, especially for the coding of complex semantic categories in large datasets via supervised machine learning. However, three characteristics so far impeded the widespread adoption of the methods in the applying disciplines: the dominance of English language models in NLP research, the necessary computing resources, and the effort required to produce training data to fine-tune PLMs. In this study, we address these challenges by using a multilingual transformer model in combination with the adapter extension to transformers, and few-shot learning methods. We test our approach on a realistic use case from communication science to automatically detect claims and arguments together with their stance in the German news debate on arms deliveries to Ukraine. In three experiments, we evaluate (1) data preprocessing strategies and model variants for this task, (2) the performance of different few-shot learning methods, and (3) how well the best setup performs on varying training set sizes in terms of validity, reliability, replicability and reproducibility of the results. We find that our proposed combination of transformer adapters with pattern exploiting training provides a parameter-efficient and easily shareable alternative to fully fine-tuning PLMs. It performs on par in terms of validity, while overall, provides better properties for application in communication studies. The results also show that pre-fine-tuning for a task on a near-domain dataset leads to substantial improvement, in particular in the few-shot setting. Further, the results indicate that it is useful to bias the dataset away from the viewpoints of specific prominent individuals.
Unified Lattice Graph Fusion for Chinese Named Entity Recognition
Authors: Dixiang Zhang, Junyu Lu, Pingjian Zhang
Date: 2023-12-28 00:00:00
Tasks: Chinese Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Integrating lexicon into character-level sequence has been proven effective to leverage word boundary and semantic information in Chinese named entity recognition (NER). However, prior approaches usually utilize feature weighting and position coupling to integrate word information, but ignore the semantic and contextual correspondence between the fine-grained semantic units in the character-word space. To solve this issue, we propose a Unified Lattice Graph Fusion (ULGF) approach for Chinese NER. ULGF can explicitly capture various semantic and boundary relations across different semantic units with the adjacency matrix by converting the lattice structure into a unified graph. We stack multiple graph-based intra-source self-attention and inter-source cross-gating fusion layers that iteratively carry out semantic interactions to learn node representations. To alleviate the over-reliance on word information, we further propose to leverage lexicon entity classification as an auxiliary task. Experiments on four Chinese NER benchmark datasets demonstrate the superiority of our ULGF approach.
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System
Authors: Mingtao Yang, See-Kiong Ng, Jinlan Fu
Date: 2023-12-28 00:00:00
Tasks: Dialogue Generation, Dialogue Management, Dialogue State Tracking, intent-classification, Intent Classification, Management, Multi-Task Learning, Question Answering, Response Generation, Transfer Learning

Pre-trained conversation models (PCMs) have demonstrated remarkable results in task-oriented dialogue (TOD) systems. Many PCMs focus predominantly on dialogue management tasks like dialogue state tracking, dialogue generation tasks like response generation, or both. However, the existing PCMs seldom consider dialogue comprehension tasks, such as dialogue question answering and summarization tasks. These tasks allow PCMs to glean dialogue context from various angles. This observation naturally raises the question: Can the performance of downstream dialogue tasks be enhanced if a PCM is pre-trained on dialogue management, generation, and comprehension tasks? To investigate this, we proposed an Omnipotent Dialogue pre-training model (OmniDialog). It unifies these three dialogue tasks into a monolithic framework by multi-task learning, fostering inter-task communication. The pre-training corpus of OmniDialog spans $\mathbf{7}$ dialogue-focused tasks, drawing from $\mathbf{15}$ datasets and encompassing over $\mathbf{3.2}$ million dialogue utterances. To our knowledge, OmniDialog is a pioneering PCM pre-trained across dialogue management, generation, and comprehension domains. We evaluated its performance across four tasks: dialogue summarization, end-to-end dialogue modeling, dialogue state tracking, and intent classification. The results underscore its efficacy in domain transfer learning, low-resource, and full-dataset scenarios. Furthermore, to glean a nuanced understanding of OmniDialog's strengths and potential pitfalls, we designed a fine-grained analysis framework for dialogue-centric tasks. Experimental results show that the OmniDialog is good at hard samples, such as long dialogues and lengthy responses.
Hiding in Plain Sight: Towards the Science of Linguistic Steganography
Authors: Leela Raj-Sankar, S. Raj Rajagopalan
Date: 2023-12-28 00:00:00
Tasks: Linguistic steganography, Sentiment Analysis

Covert communication (also known as steganography) is the practice of concealing a secret inside an innocuous-looking public object (cover) so that the modified public object (covert code) makes sense to everyone but only someone who knows the code can extract the secret (message). Linguistic steganography is the practice of encoding a secret message in natural language text such as spoken conversation or short public communications such as tweets.. While ad hoc methods for covert communications in specific domains exist ( JPEG images, Chinese poetry, etc), there is no general model for linguistic steganography specifically. We present a novel mathematical formalism for creating linguistic steganographic codes, with three parameters: Decodability (probability that the receiver of the coded message will decode the cover correctly), density (frequency of code words in a cover code), and detectability (probability that an attacker can tell the difference between an untampered cover compared to its steganized version). Verbal or linguistic steganography is most challenging because of its lack of artifacts to hide the secret message in. We detail a practical construction in Python of a steganographic code for Tweets using inserted words to encode hidden digits while using n-gram frequency distortion as the measure of detectability of the insertions. Using the publicly accessible Stanford Sentiment Analysis dataset we implemented the tweet steganization scheme -- a codeword (an existing word in the data set) inserted in random positions in random existing tweets to find the tweet that has the least possible n-gram distortion. We argue that this approximates KL distance in a localized manner at low cost and thus we get a linguistic steganography scheme that is both formal and practical and permits a tradeoff between codeword density and detectability of the covert message.
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition
Authors: Yuntao Shou, Tao Meng, Wei Ai, Nan Yin, Keqin Li
Date: 2023-12-28 00:00:00
Tasks: Contrastive Learning, Emotion Recognition, Graph Representation Learning, Multimodal Emotion Recognition, Representation Learning

With the release of increasing open-source emotion recognition datasets on social media platforms and the rapid development of computing resources, multimodal emotion recognition tasks (MER) have begun to receive widespread research attention. The MER task extracts and fuses complementary semantic information from different modalities, which can classify the speaker's emotions. However, the existing feature fusion methods have usually mapped the features of different modalities into the same feature space for information fusion, which can not eliminate the heterogeneity between different modalities. Therefore, it is challenging to make the subsequent emotion class boundary learning. To tackle the above problems, we have proposed a novel Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive for Multimodal Emotion Recognition (AR-IIGCN) method. Firstly, we input video, audio, and text features into a multi-layer perceptron (MLP) to map them into separate feature spaces. Secondly, we build a generator and a discriminator for the three modal features through adversarial representation, which can achieve information interaction between modalities and eliminate heterogeneity among modalities. Thirdly, we introduce contrastive graph representation learning to capture intra-modal and inter-modal complementary semantic information and learn intra-class and inter-class boundary information of emotion categories. Specifically, we construct a graph structure for three modal features and perform contrastive representation learning on nodes with different emotions in the same modality and the same emotion in different modalities, which can improve the feature representation ability of nodes. Extensive experimental works show that the ARL-IIGCN method can significantly improve emotion recognition accuracy on IEMOCAP and MELD datasets.
Graph Neural Networks for Antisocial Behavior Detection on Twitter
Authors: Martina Toshevska, Slobodan Kalajdziski, Sonja Gievska
Date: 2023-12-28 00:00:00

Social media resurgence of antisocial behavior has exerted a downward spiral on stereotypical beliefs, and hateful comments towards individuals and social groups, as well as false or distorted news. The advances in graph neural networks employed on massive quantities of graph-structured data raise high hopes for the future of mediating communication on social media platforms. An approach based on graph convolutional data was employed to better capture the dependencies between the heterogeneous types of data. Utilizing past and present experiences on the topic, we proposed and evaluated a graph-based approach for antisocial behavior detection, with general applicability that is both language- and context-independent. In this research, we carried out an experimental validation of our graph-based approach on several PAN datasets provided as part of their shared tasks, that enable the discussion of the results obtained by the proposed solution.
A Reversible Perspective on Petri Nets and Event Structures
Authors: Hernán Melgratti, Claudio Antares Mezzina, G. Michele Pinna
Date: 2023-12-27 00:00:00

Event structures have emerged as a foundational model for concurrent computation, explaining computational processes by outlining the events and the relationships that dictate their execution. They play a pivotal role in the study of key aspects of concurrent computation models, such as causality and independence, and have found applications across a broad range of languages and models, spanning realms like persistence, probabilities, and quantum computing. Recently, event structures have been extended to address reversibility, where computational processes can undo previous computations. In this context, reversible event structures provide abstract representations of processes capable of both forward and backward steps in a computation. Since their introduction, event structures have played a crucial role in bridging operational models, traditionally exemplified by Petri nets and process calculi, with denotational ones, i.e., algebraic domains. In this context, we revisit the standard connection between Petri nets and event structures under the lenses of reversibility. Specifically, we introduce a subset of contextual Petri nets, dubbed reversible causal nets, that precisely correspond to reversible prime event structures. The distinctive feature of reversible causal nets lies in deriving causality from inhibitor arcs, departing from the conventional dependence on the overlap between the post and preset of transitions. In this way, we are able to operationally explain the full model of reversible prime event structures.
Make BERT-based Chinese Spelling Check Model Enhanced by Layerwise Attention and Gaussian Mixture Model
Authors: Yongchang Cao, Liang He, Zhen Wu, Xinyu Dai
Date: 2023-12-27 00:00:00
Tasks: model, Part-Of-Speech Tagging, POS, POS Tagging

BERT-based models have shown a remarkable ability in the Chinese Spelling Check (CSC) task recently. However, traditional BERT-based methods still suffer from two limitations. First, although previous works have identified that explicit prior knowledge like Part-Of-Speech (POS) tagging can benefit in the CSC task, they neglected the fact that spelling errors inherent in CSC data can lead to incorrect tags and therefore mislead models. Additionally, they ignored the correlation between the implicit hierarchical information encoded by BERT's intermediate layers and different linguistic phenomena. This results in sub-optimal accuracy. To alleviate the above two issues, we design a heterogeneous knowledge-infused framework to strengthen BERT-based CSC models. To incorporate explicit POS knowledge, we utilize an auxiliary task strategy driven by Gaussian mixture model. Meanwhile, to incorporate implicit hierarchical linguistic knowledge within the encoder, we propose a novel form of n-gram-based layerwise self-attention to generate a multilayer representation. Experimental results show that our proposed framework yields a stable performance boost over four strong baseline models and outperforms the previous state-of-the-art methods on two datasets.