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31st International Conference on Computational Linguistics, COLING 2025

作者： Li, Yutong Chen, Lu Liu, Aiwei Yu, Kai Wen, Lijie Tsinghua University Beijing China X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761964

The literature review is an indispensable step in the research process. It provides the benefit of comprehending the research problem and understanding the current research situation while conducting a comparative analysis of prior works. However, literature summary is challenging and time consuming. The previous LLM-based studies on literature review mainly focused on the complete process, including literature retrieval, screening, and summarization. However, for the summarization step, simple CoT method often lacks the ability to provide extensive comparative summary. In this work, we firstly focus on the independent literature summarization step and introduce ChatCite, an LLM agent with human workflow guidance for comparative literature summary. This agent, by mimicking the human workflow, first extracts key elements from relevant literature and then generates summaries using a Reflective Incremental Mechanism. In order to better evaluate the quality of the generated summaries, we devised a LLM-based automatic evaluation metric, G-Score, in refer to the human evaluation criteria. The ChatCite agent outperformed other models in various dimensions in the experiments. The literature summaries generated by ChatCite can also be directly used for drafting literature *** code is available. © 2025 Association for Computational Linguistics.

关键词： Chatbots

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Large circuit models: opportunities and challenges

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science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation 57

COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Wang, Zongwu Liu, Fangxin Yang, Ning Huang, Shiyuan Li, Haomin Jiang, Li Shanghai Jiao Tong University Department of Computer Science and Engineering China Shanghai Qi Zhi Institute China AI Institute Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence China

ISBN: (纸本)9798350350579

Brain-inspired spiking neural networks (SNNs) are considered energy-efficient alternatives to conventional deep neural networks (DNNs). By adopting event-driven information processing, SNNs can significantly reduce the computational demands associated with DNNs, while still achieving comparable performance. However, current SNNs primarily prioritize high accuracy by constructing complex neuron models that generate sparse spikes. Unfortunately, this approach results in low energy efficiency and high latency, posing a significant challenge for deploying SNNs at the edge. Furthermore, the dominant computation in SNNs, which involves spike-wise Accumulate-Compare operations, is well-suited for Computing-in-Memory (CIM) architectures. However, exploiting high parallel processing and spike sparsity in CIM-based SNN accelerators is challenging due to the irregularity and time dependency of spikes. To address these limitations, the paper proposes COMPASS, a SRAM-based CIM architecture for efficient SNNs. We first introduce an efficient method to exploit irregular sparsity for both input spikes (explicit) and output spikes (implicit). This is achieved through a speculation mechanism that exploit dynamic spike patterns, enabling lean hardware for sparsity utilization. Additionally, the CIM architecture is carefully modified to facilitate dynamic spike pattern generation and exploitation with minimal overhead. Moreover, we design an adaptive dataflow with temporal spike representation tailored for input/output spikes, reducing memory footprint and enabling parallel execution. Comprehensive evaluation results demonstrate that COMPASS can achieve 26.7x end-to-end speedup over recent SNN accelerators hardware implementation with up to 386.7x less energy per inference. © 2024 IEEE.

关键词： Compasses (magnetic)

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Code-Switching Text Generation and Injection in Mandarin-English ASR 48

Code-Switching Text Generation and Injection in Mandarin-Eng...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yu, Haibin Hu, Yuxuan Qian, Yao Jin, Ma Liu, Linquan Liu, Shujie Shi, Yu Qian, Yanmin Lin, Edward Zeng, Michael Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering China Microsoft Corporation United States

ISBN: (纸本)9781728163277

Code-switching speech refers to a means of expression by mixing two or more languages within a single utterance. Automatic Speech Recognition (ASR) with End-to-End (E2E) modeling for such speech can be a challenging task due to the lack of data. In this study, we investigate text generation and injection for improving the performance of an industry commonly-used streaming model, Transformer-Transducer (T-T), in Mandarin-English code-switching speech recognition. We first propose a strategy to generate codeswitching text data and then investigate injecting generated text into T-T model explicitly by Text-To-Speech (TTS) conversion or implicitly by tying speech and text latent spaces. Experimental results on the T-T model trained with a dataset containing 1,800 hours of real Mandarin-English code-switched speech show that our approaches to inject generated code-switching text significantly boost the performance of T-T models, i.e., 16% relative Token-based Error Rate (TER) reduction averaged on three evaluation sets, and the approach of tying speech and text latent spaces is superior to that of TTS conversion on the evaluation set which contains more homogeneous data with the training set. © 2023 IEEE.

关键词： Transducers

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Iterative Noisy-Target Approach: Speech Enhancement Without Clean Speech 18th

Iterative Noisy-Target Approach: Speech Enhancement Without ...

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18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Zhang, Yifan Jiang, Wenbin Zhuo, Qing Yu, Kai MoE Key Lab of Artificial Intelligence X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Communication Engineering Hangzhou Dianzi University Hangzhou China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9789819706006

Traditional Deep Neural Network based speech enhancement usually requires clean speech as the target of training. However, limited access to ideal clean speech hinders its practical use. Meanwhile, existing self-supervised or unsupervised methods face both unsatisfactory performance and impractical source demand (e.g., various kinds of noises added to the same clean speech). Hence, there’s a significant need to either release the restriction of training data or improve the performance. In this paper, we propose a training strategy that only requires noisy speech and noise waveform. It primarily consists of two phases: 1) With a pair of input and target constructed by adding noise to noisy speech itself for the training of DNN, the first round of training uses noisier speech (noise added to noisy speech) and noisy speech 2) For the following training, using the model trained last time to refine the noisy speech, construct new noisier-noisy pairs for next turn of training. Moreover, to accelerate the process, we apply the iteration into epochs. To evaluate the efficiency, we utilized a dataset including 10 types of real-world noises and made a comparison with two classic supervised and unsupervised methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Speech enhancement

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Front-End Adapter: Adapting Front-End Input of Speech Based Self-Supervised Learning for Speech Recognition 48

Front-End Adapter: Adapting Front-End Input of Speech Based ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Xie Ma, Ziyang Tang, Changli Wang, Yujin Zheng, Zhisheng Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-Lance Lab Department of Computer Science and Engineering Shanghai China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9781728163277

Recent years have witnessed a boom in self-supervised learning (SSL) in various areas including speech processing. Speech based SSL models present promising performance in a range of speech related tasks. However, the training of SSL models is computationally expensive and a common practice is to fine-tune a released SSL model on the specific task. It is essential to use consistent front-end input during pre-training and fine-tuning. This consistency may introduce potential issues when the optimal front-end is not the same as that used in pre-training. In this paper, we propose a simple but effective front-end adapter to address this front-end discrepancy. By minimizing the distance between the outputs of different front-ends, the filterbank feature (Fbank) can be compatible with SSL models which are pre-trained with waveform. The experiment results demonstrate the effectiveness of our proposed front-end adapter on several popular SSL models for the speech recognition task. © 2023 IEEE.

关键词： Adapter Automatic Speech Recognition Front-end Feature Self-Supervised Learning

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An Approximation for Routing Planning, Mobile Charging, and Energy Sharing for Sensing Devices

An Approximation for Routing Planning, Mobile Charging, and ...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Zifeng Kong, Dejun Gao, Yucen Dai, Haipeng Gao, Xiaofeng He, Tian Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Nanjing University Department of Computer Science and Technology Nanjing China University of Minnesota Department of Computer Science and Technology MN United States

ISBN: (纸本)9798350304855

Wireless Charging Vehicles (WCVs) have been widely explored as a means of enabling continuous operation of sensors that are powered by batteries. However, the energy consumption of WCVs can be inefficient, leading to insufficient energy supply for sensors that are located in challenging-to-access areas. Consequently, there is a need to design an effective charging and energy sharing scheme for sensors to improve the quality of service in this setup. This paper focuses on the Joint optimization of Mobile charging and Energy sharing of sensors (JOIN-ME) problem, which is known to be NP-hard. To address this challenge, we first transform JOIN-ME into a submodular maximization problem with general constraints. Subsequently, we propose the Routing planning, Mobile charging, and Energy sharing for Sensing devices (RMES) algorithm, which has an approximation ratio of 1/8(1-1/e). Finally, we conduct experiments to showcase the superior performance of RMES compared to existing baselines, under varying scales and constraints. Our work on the design of an efficient charging and energy sharing scheme for sensors can significantly improve the reliability and longevity of wireless sensor networks, enabling the deployment of these networks in critical applications such as environmental monitoring, crowd sensing, and security surveillance. © 2023 IEEE.

关键词： Approximation algorithms

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Target Sound Extraction with Variable Cross-Modality Clues 48

Target Sound Extraction with Variable Cross-Modality Clues

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Chenda Qian, Yao Chen, Zhuo Wang, Dongmei Yoshioka, Takuya Liu, Shujie Qian, Yanmin Zeng, Michael Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering China Microsoft RedmondWA United States

ISBN: (纸本)9781728163277

Automatic target sound extraction (TSE) is a machine learning approach to mimic the human auditory perception capability of attending to a sound source of interest from a mixture of sources. It often uses a model conditioned on a fixed form of target sound clues, such as a sound class label, which limits the ways in which users can interact with the model to specify the target sounds. To leverage variable number of clues cross modalities available in the inference phase, including a video, a sound event class, and a text caption, we propose a unified transformer-based TSE model architecture, where a multi-clue attention module integrates all the clues across the modalities. Since there is no off-the-shelf benchmark to evaluate our proposed approach, we build a dataset1 based on public corpora, Audioset and AudioCaps. Experimental results for seen and unseen target-sound evaluation sets show that our proposed TSE model can effectively deal with a varying number of clues which improves the TSE performance and robustness against partially compromised clues. © 2023 IEEE.

关键词： cross-modality attention multi-clue processing Target sound extraction

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Joint Discriminator and Transfer Based Fast Domain Adaptation For End-To-End Speech Recognition 48

Joint Discriminator and Transfer Based Fast Domain Adaptatio...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Shao, Hang Tan, Tian Wang, Wei Gong, Xun Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China AISpeech Ltd Suzhou China

ISBN: (纸本)9781728163277

Adapting End-to-End (E2E) models to unseen domains is still a big challenge since training E2E models requires lots of paired audio and text training data. We propose a novel domain adaptation framework for the E2E model, which only uses the text of the target domain. Moreover, the proposed methods can keep the performance on the source domain intact while greatly improving the performance on the target domain. The proposed framework consists of two parts: the discriminator and the transfer which were optimized separately. Finally, optimized discriminator and transfer were combined and evaluated on two domain adaption tasks. In the experiments of adapting the English Librispeech to Gigaspeech, we obtained an average relative 11.6% and 11.8% on word error rate (WER) reduction for the target domain dev and test sets, respectively, while almost without WER degradation on the source domain. For the inhouse Chinese corpus aviation and TV, the character error rate (CER) of the source domain increased within 5%, while the CER on the target domain achieved around relative 85% and 42% improvement, respectively. In addition, our approach is also more effective in the mixed domain scenarios in the evaluation. © 2023 IEEE.

关键词： discriminator and transfer domain adaptation end-to-end speech recognition log-likelihood ratio

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Automatic Fusion Network for Cold-start CVR Prediction with Explicit Multi-Level Representation 39

Automatic Fusion Network for Cold-start CVR Prediction with ...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Jin, Jipeng Lu, Guangben Li, Sijia Gao, Xiaofeng Tan, Ao Wang, Lifeng Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Dept. of Computer Science and Engineering China Corporate Development Group Tencent China

ISBN: (纸本)9798350322279

Estimating conversion rate (CVR) accurately has been one of the most central problems in online advertising. Existing methods in production focus on learning effective interactions among features to boost the model performance. Despite great success, these methods treat all the features equally without distinction. However, different features suffer differently from cold-start issues. Tail elements in those high-cardinality features, which we denote as fine-grained features, tend to have inadequate samples and thus fail to obtain semantically meaningful embeddings. Interacting with those features leads astray and impairs the accuracy of new ads in a cold-start scenario. In this paper, we propose Automatic Fusion Network (AutoFuse) to better tackle the challenge. AutoFuse explicitly separates features into groups based on their granularity and learns multiple levels of representation conditioned on different combinations of feature groups. Concretely, AutoFuse learns an ad-level representation to depict the unique individual character and a group-level representation to portray the collective information by discarding the fine-grained features. The final robust and general ad representation is obtained by integrating these two level representations adaptively. Such a combination encompasses a wider amount of information, and thereby mitigates the cold-start issue. Extensive experiments on two industrial-scale datasets and three public datasets show that AutoFuse significantly and consistently outperforms a spectrum of competitive methods including our currently deployed model. Meanwhile, the remarkable improvement on new ads validates the effectiveness of our method in cold-start scenarios. We design AutoFuse as a generic approach and thus it can be seamlessly transferred into other domains. Our method has been deployed online to serve billions of users and ads and has achieved significant GMV gain of 2.84%. © 2023 IEEE.

关键词： Marketing

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