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Yiping Song

Yiping Song appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Computation and LanguageMachine LearningArtificial Intelligenceeess.IVHuman-Computer Interactionphysics.optics

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Published work

9 published item(s)

preprint2026arXiv

ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models

Large language models inevitably retain sensitive information, defined as inputs that may induce harmful generations, due to training on massive web corpora, raising concerns for privacy and safety. Existing machine unlearning methods primarily rely on retraining or aggressive fine-tuning, which are either computationally expensive or prone to degrading related knowledge and overall model utility. In this work, we reformulate machine unlearning as a precise knowledge re-mapping problem via model editing. We propose ZeroUnlearn, a few-shot unlearning framework. It overwrites sensitive inputs by mapping them to a neutral target state and removing their original representations. ZeroUnlearn enforces representational orthogonality through a multiplicative parameter update with a closed-form solution, enabling efficient and targeted unlearning. We further extend ZeroUnlearn to a gradient-based variant for multi-sample unlearning. Experiments demonstrate that our approach outperforms existing baselines while preserving general model utility. Our code is available at the github: https://github.com/XMUDeepLIT/ZeroUnlearn.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation

Building models of natural language processing (NLP) is challenging in low-resource scenarios where only limited data are available. Optimization-based meta-learning algorithms achieve promising results in low-resource scenarios by adapting a well-generalized model initialization to handle new tasks. Nonetheless, these approaches suffer from the memorization overfitting issue, where the model tends to memorize the meta-training tasks while ignoring support sets when adapting to new tasks. To address this issue, we propose a memory imitation meta-learning (MemIML) method that enhances the model's reliance on support sets for task adaptation. Specifically, we introduce a task-specific memory module to store support set information and construct an imitation module to force query sets to imitate the behaviors of some representative support-set samples stored in the memory. A theoretical analysis is provided to prove the effectiveness of our method, and empirical results also demonstrate that our method outperforms competitive baselines on both text classification and generation tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Improved mathematical models of structured-light modulation analysis technique for contaminant and defect detection

Surface quality inspection of optical components is critical in optical and electronic industries. Structured-Light Modulation Analysis Technique (SMAT) is a novel method recently proposed for the contaminant and defect detection of specular surfaces and transparent objects, and this approach was verified to be effective in eliminating ambient light. The mechanisms and mathematical models of SMAT were analyzed and established based on the theory of photometry and the optical characteristics of contaminants and defects. However, there are still some phenomena exist as conundrums in actual detection process, which cannot be well explained. In order to better analyze the phenomena in practical circumstances, improved mathematical models of SMAT are constructed based on the surface topography of contaminants and defects in this paper. These mathematical models can be used as tools for analyzing various contaminants and defects in different systems, and provide effective instruction for subsequent work. Simulations and experiments on the modulation and the luminous flux of fringe patterns have been implemented to verify the validity of these mathematical models. In adddition, by using the fringe patterns with mutually perpendicular sinusoidal directions, two obtained modulation images can be merged to solve the incomplete information acquisition issue caused by the differentiated response of modulation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks

Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems. Existing methods tend to use the meta-learning framework which pre-trains the parameters on all non-target tasks then fine-tunes on the target task. However, fine-tuning distinguishes tasks from the parameter perspective but ignores the model-structure perspective, resulting in similar dialogue models for different tasks. In this paper, we propose an algorithm that can customize a unique dialogue model for each task in the few-shot setting. In our approach, each dialogue model consists of a shared module, a gating module, and a private module. The first two modules are shared among all the tasks, while the third one will differentiate into different network structures to better capture the characteristics of the corresponding task. The extensive experiments on two datasets show that our method outperforms all the baselines in terms of task consistency, response quality, and diversity.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases

Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for directly training deep learning models on individual disease data or all the data from different diseases. Mortality prediction for these patients with different diseases can be viewed as a multi-task learning problem with insufficient data and large task number. But the tasks with little training data also make it hard to train task-specific modules in multi-task learning models. To address the challenges of data insufficiency and task diversity, we propose an initialization-sharing multi-task learning method (Ada-Sit) which learns the parameter initialization for fast adaptation to dynamically measured similar tasks. We use Ada-Sit to train long short-term memory networks (LSTM) based prediction models on longitudinal EHR data. And experimental results demonstrate that the proposed model is effective for mortality prediction of diverse rare diseases.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation

Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing by Reading (CbR), where conversations take place with respect to a given external document. In previous work, the external document is utilized by (1) creating a context-aware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory. In this paper, we propose to create the document memory with some anticipated responses in mind. This is achieved using a teacher-student framework. The teacher is given the external document, the context, and the ground-truth response, and learns how to build a response-aware document memory from three sources of information. The student learns to construct a response-anticipated document memory from the first two sources, and the teacher's insight on memory creation. Empirical results show that our model outperforms the previous state-of-the-art for the CbR task.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Dialogue Session Segmentation by Embedding-Enhanced TextTiling

In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation. However, it is unwise to track all previous utterances in the current session as not all of them are equally important. In this paper, we address the problem of session segmentation. We propose an embedding-enhanced TextTiling approach, inspired by the observation that conversation utterances are highly noisy, and that word embeddings provide a robust way of capturing semantics. Experimental results show that our approach achieves better performance than the TextTiling, MMD approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation

Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based generative dialogue systems. We first use pointwise mutual information (PMI) to predict a noun as a keyword, reflecting the main gist of the reply. We then propose seq2BF, a "sequence to backward and forward sequences" model, which generates a reply containing the given keyword. Experimental results show that our approach significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure, and that the predicted keyword can appear at an appropriate position in the reply.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems

Open-domain human-computer conversation has attracted much attention in the field of NLP. Contrary to rule- or template-based domain-specific dialog systems, open-domain conversation usually requires data-driven approaches, which can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (called a query) in a large database, and return a reply that best matches the query. Generative approaches, typically based on recurrent neural networks (RNNs), can synthesize new replies, but they suffer from the problem of generating short, meaningless utterances. In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain. In our approach, the retrieved candidate, in addition to the original query, is fed to an RNN-based reply generator, so that the neural model is aware of more information. The generated reply is then fed back as a new candidate for post-reranking. Experimental results show that such ensemble outperforms each single part of it by a large margin.

0 citations0 reviewsNo code linkedOpen access