Author name cluster

Wenge Rong

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9 papers

2 author rows

AAAI Conference 2025 Conference Paper

Enhancing LLMs via High-Knowledge Data Selection

Feiyu Duan
Xuemiao Zhang
Sirui Wang
Haoran Que
Yuqi Liu
Wenge Rong
Xunliang Cai

The performance of Large Language Models (LLMs) is intrinsically linked to the quality of its training data. Although several studies have proposed methods for high-quality data selection, they do not consider the importance of knowledge richness in text corpora. In this paper, we propose a novel and gradient-free High-Knowledge Scorer (HKS) to select high-quality data from the dimension of knowledge, to alleviate the problem of knowledge scarcity in the pre-trained corpus. We propose a comprehensive multi-domain knowledge element pool and introduce knowledge density and coverage as metrics to assess the knowledge content of the text. Based on this, we propose a comprehensive knowledge scorer to select data with intensive knowledge, which can also be utilized for domain-specific high-knowledge data selection by restricting knowledge elements to the specific domain. We train models on a high-knowledge bilingual dataset, and experimental results demonstrate that our scorer improves the model's performance in knowledge-intensive and general comprehension tasks, and is effective in enhancing both the generic and domain-specific capabilities of the model.

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NeurIPS Conference 2024 Conference Paper

RoleAgent: Building, Interacting, and Benchmarking High-quality Role-Playing Agents from Scripts

Jiaheng Liu
Zehao Ni
Haoran Que
Tao Sun
Zekun Wang
Jian Yang
Jiakai Wang
Hongcheng Guo

Believable agents can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication. Recently, generative agents have been proposed to simulate believable human behavior by using Large Language Models. However, the existing method heavily relies on human-annotated agent profiles (e. g. , name, age, personality, relationships with others, and so on) for the initialization of each agent, which cannot be scaled up easily. In this paper, we propose a scalable RoleAgent framework to generate high-quality role-playing agents from raw scripts, which includes building and interacting stages. Specifically, in the building stage, we use a hierarchical memory system to extract and summarize the structure and high-level information of each agent for the raw script. In the interacting stage, we propose a novel innovative mechanism with four steps to achieve a high-quality interaction between agents. Finally, we introduce a systematic and comprehensive evaluation benchmark called RoleAgentBench to evaluate the effectiveness of our RoleAgent, which includes 100 and 28 roles for 20 English and 5 Chinese scripts, respectively. Extensive experimental results on RoleAgentBench demonstrate the effectiveness of RoleAgent.

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IJCAI Conference 2023 Conference Paper

Discovering Sounding Objects by Audio Queries for Audio Visual Segmentation

Shaofei Huang
Han Li
Yuqing Wang
Hongji Zhu
Jiao Dai
Jizhong Han
Wenge Rong
Si Liu

Audio visual segmentation (AVS) aims to segment the sounding objects for each frame of a given video. To distinguish the sounding objects from silent ones, both audio-visual semantic correspondence and temporal interaction are required. The previous method applies multi-frame cross-modal attention to conduct pixel-level interactions between audio features and visual features of multiple frames simultaneously, which is both redundant and implicit. In this paper, we propose an Audio-Queried Transformer architecture, AQFormer, where we define a set of object queries conditioned on audio information and associate each of them to particular sounding objects. Explicit object-level semantic correspondence between audio and visual modalities is established by gathering object information from visual features with predefined audio queries. Besides, an Audio-Bridged Temporal Interaction module is proposed to exchange sounding object-relevant information among multiple frames with the bridge of audio features. Extensive experiments are conducted on two AVS benchmarks to show that our method achieves state-of-the-art performances, especially 7. 1% M_J and 7. 6% M_F gains on the MS3 setting.

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ICLR Conference 2023 Conference Paper

Transformer-Patcher: One Mistake Worth One Neuron

Zeyu Huang
Yikang Shen
Xiaofeng Zhang 0004
Jie Zhou
Wenge Rong
Zhang Xiong 0001

Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For a model deployed in an industrial environment, fixing these mistakes quickly and robustly is vital to improve user experiences. Previous works formalize such problems as Model Editing (ME) and mostly focus on fixing one mistake. However, the one-mistake-fixing scenario is not an accurate abstraction of the real-world challenge. In the deployment of AI services, there are ever-emerging mistakes, and the same mistake may recur if not corrected in time. Thus a preferable solution is to rectify the mistakes as soon as they appear nonstop. Therefore, we extend the existing ME into the Sequential Model Editing (SME) to help develop more practical editing methods. Our study shows that current ME methods either fail to make a sequence of edits or to remember previous edits. We then introduce Transformer-Patcher, a novel model editor that can shift the behavior of transformer-based models by simply adding and training a few neurons in the last Feed-Forward Network layer. Experimental results on both classification and generation tasks show that Transformer-Patcher can successively correct up to thousands of errors (Reliability) and generalize to their equivalent inputs (Generality) while retaining the model’s accuracy on irrelevant inputs (Locality). Our method outperforms previous fine-tuning and HyperNetwork-based methods and achieves state-of-the-art performance for Sequential Model Editing (SME).

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NeurIPS Conference 2022 Conference Paper

Improving Variational Autoencoders with Density Gap-based Regularization

Jianfei Zhang
Jun Bai
Chenghua Lin
Yanmeng Wang
Wenge Rong

Variational autoencoders (VAEs) are one of the most powerful unsupervised learning frameworks in NLP for latent representation learning and latent-directed generation. The classic optimization goal of VAEs is to maximize the Evidence Lower Bound (ELBo), which consists of a conditional likelihood for generation and a negative Kullback-Leibler (KL) divergence for regularization. In practice, optimizing ELBo often leads the posterior distribution of all samples converging to the same degenerated local optimum, namely posterior collapse or KL vanishing. There are effective ways proposed to prevent posterior collapse in VAEs, but we observe that they in essence make trade-offs between posterior collapse and the hole problem, i. e. , the mismatch between the aggregated posterior distribution and the prior distribution. To this end, we introduce new training objectives to tackle both problems through a novel regularization based on the probabilistic density gap between the aggregated posterior distribution and the prior distribution. Through experiments on language modeling, latent space visualization, and interpolation, we show that our proposed method can solve both problems effectively and thus outperforms the existing methods in latent-directed generation. To the best of our knowledge, we are the first to jointly solve the hole problem and posterior collapse.

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AAAI Conference 2018 Conference Paper

Improved Text Matching by Enhancing Mutual Information

Yang Liu
Wenge Rong
Zhang Xiong

Text matching is a core issue for question answering (QA), information retrieval (IR) and many other ﬁelds. We propose to reformulate the original text, i. e. , generating a new text that is semantically equivalent to original text, to improve text matching degree. Intuitively, the generated text improves mutual information between two text sequences. We employ the generative adversarial network as the reformulation model where there is a discriminator to guide the text generating process. In this work, we focus on matching question and answers. The task is to rank answers based on QA matching degree. We ﬁrst reformulate the original question without changing the asker’s intent, then compute a relevance score for each answer. To evaluate the method, we collected questions and answers from Zhihu. In addition, we also conduct substantial experiments on public data such as SemEval and WikiQA to compare our method with existing methods. Experimental results demonstrate that after adding the reformulated question, the ranking performance across different matching models can be improved consistently, indicating that the reformulated question has enhanced mutual information and effectively bridged the semantic gap between QA.

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IJCAI Conference 2017 Conference Paper

Exploration of Tree-based Hierarchical Softmax for Recurrent Language Models

Nan Jiang
Wenge Rong
Min Gao
Yikang Shen
Zhang Xiong

Recently, variants of neural networks for computational linguistics have been proposed and successfully applied to neural language modeling and neural machine translation. These neural models can leverage knowledge from massive corpora but they are extremely slow as they predict candidate words from a large vocabulary during training and inference. As an alternative to gradient approximation and softmax with class decomposition, we explore the tree-based hierarchical softmax method and reform its architecture, making it compatible with modern GPUs and introducing a compact tree-based loss function. When combined with several word hierarchical clustering algorithms, improved performance is achieved in language modelling task with intrinsic evaluation criterions on PTB, WikiText-2 and WikiText-103 datasets.

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AAAI Conference 2017 Conference Paper

Word Embedding Based Correlation Model for Question/Answer Matching

Yikang Shen
Wenge Rong
Nan Jiang
Baolin Peng
Jie Tang
Zhang Xiong

The large scale of Q&A archives accumulated in community based question answering (CQA) servivces are important information and knowledge resource on the web. Question and answer matching task has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, a Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding. Given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs, while it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method’s promising potential.

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AAAI Conference 2015 Conference Paper

Question/Answer Matching for CQA System via Combining Lexical and Sequential Information

Yikang Shen
Wenge Rong
Zhiwei Sun
Yuanxin Ouyang
Zhang Xiong

Community-based Question Answering (CQA) has become popular in knowledge sharing sites since it allows users to get answers to complex, detailed, and personal questions directly from other users. Large archives of historical questions and associated answers have been accumulated. Retrieving relevant historical answers that best match a question is an essential component of a CQA service. Most state of the art approaches are based on bag-of-words models, which have been proven successful in a range of text matching tasks, but are insufficient for capturing the important word sequence information in short text matching. In this paper, a new architecture is proposed to more effectively model the complicated matching relations between questions and answers. It utilises a similarity matrix which contains both lexical and sequential information. Afterwards the information is put into a deep architecture to find potentially suitable answers. The experimental study shows its potential in improving matching accuracy of question and answer.

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