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Exploiting Differential-Based Data Encoding for Enhanced Query Efficiency 25

Exploiting Differential-Based Data Encoding for Enhanced Que...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

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

ISBN: (纸本)9798400706356

Storing large-scale high-dimensional data, which is rapidly generated by both industry and academia, poses substantial challenges, primarily in terms of storage and maintenance costs. While data compression techniques offer a potential solution to these challenges, they must overcome two critical hurdles: 1) preserving data integrity within lossless bounds and 2) maintaining query performance on compressed *** address the above challenges, we propose a novel approach based on differential techniques that combine high-dimensional data compression with an efficient query engine. Specifically, Our methodology begins by employing vector quantization to transform high-dimensional vectors into compact integer sequences, known as quantization codes - -a classical and versatile compression method. Subsequently, we introduce DCQ, which builds upon the differential concept to perform lossless compression on quantization codes. DCQ organizes quantization codes into a tree structure and constructs a hierarchy of trees by analyzing the dissimilarity between two quantization codes. To minimize the impact on query performance, we have developed an adaptive reconstruction algorithm that partitions and reconstructs the original tree structure. This algorithm effectively balances the workload for searching each sub-tree, optimizing the compression-performance trade-off. Finally, we adopt a storage format based on multiple subtrees for rapid searching, thereby enabling differential storage to facilitate efficient queries. Extensive experiments on various large-scale real-world datasets show that DCQ achieves a compression ratio of up to 2.5 on the quantization codes and achieves > 50× performance improvement compared to the state-of-the-art general-purpose lossless compression techniques. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Trees (mathematics)

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ALIGNSUM: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference

ALIGNSUM: Data Pyramid Hierarchical Fine-tuning for Aligning...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Han, Yang Wang, Yiming Wang, Rui Chen, Lu Yu, Kai X-LANCE Lab Department of Computer Science and Engineering SJTU China MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761681

Text summarization tasks commonly employ Pre-trained Language Models (PLMs) to fit diverse standard *** these PLMs excel in automatic evaluations, they frequently underperform in human evaluations, indicating a deviation between their generated summaries and human summarization *** discrepancy is likely due to the low quality of fine-tuning datasets and the limited availability of high-quality human-annotated data that reflect true human *** address this challenge, we introduce a novel human summarization preference alignment framework *** framework consists of three parts: Firstly, we construct a Data Pymarid with extractive, abstractive, and human-annotated summary ***, we conduct the Gaussian Resampling to remove summaries with extreme ***, we implement the two-stage hierarchical fine-tuning with Data Pymarid after Gaussian *** apply ALIGNSUM to PLMs on the human-annotated CNN/DailyMail and BBC XSum *** show that with ALIGNSUM, PLMs like BART-Large surpass 175B GPT-3 in both automatic and human *** demonstrates that ALIGNSUM significantly enhances the alignment of language models with human summarization preferences. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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INTERPGNN: UNDERSTAND AND IMPROVE GENERALIZATION ABILITY OF TRANSDUCTIVE GNNS THROUGH THE LENS OF INTERPLAY BETWEEN TRaiN AND TEST NODES 12

INTERPGNN: UNDERSTAND AND IMPROVE GENERALIZATION ABILITY OF ...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Sun, Jiawei Li, Kailai Chen, Ruoxin Li, Jie Wu, Chentao Ding, Yue Yan, Junchi Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of AI Shanghai Jiao Tong University China Yancheng Blockchain Research Institute China Youtu Lab Tencent China

Transductive node prediction has been a popular learning setting in Graph Neural Networks (GNNs). It has been widely observed that the shortage of information flow between the distant nodes and out-of-batch nodes (for large-scale graphs) often hurt the generalization of GNNs which overwhelmingly adopt message-passing. Yet there is still no formal and direct theoretical results to quantitatively capture the underlying mechanism, despite the recent advance in both theoretical and empirical studies for GNN's generalization ability. In this paper, the L-hop interplay (i.e., message passing capability with training nodes) for an L-layer GNN is successfully incorporated in our derived PAC-Bayesian bound for GNNs in the semi-supervised transductive setting. In other words, we quantitatively show how the interplay between training and testing sets influence the generalization ability which also partly explains the effectiveness of some existing empirical methods for enhancing generalization. Based on this result, we further design a plug-and-play Graph Global Workspace module for GNNs (InterpGNN-GW) to enhance the interplay, utilizing the key-value attention mechanism to summarize crucial nodes' embeddings into memory and broadcast the memory to all nodes, in contrast to the pairwise attention scheme in previous graph transformers. Extensive experiments on both small-scale and large-scale graph datasets validate the effectiveness of our theory and approaches. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Flow graphs

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ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary 31

ChatCite: LLM Agent with Human Workflow Guidance for Compara...

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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 indep.ndent 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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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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GoT: Effective Graph-of-Thought Reasoning in Language Models

GoT: Effective Graph-of-Thought Reasoning in Language Models

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

ISBN: (纸本)9798891761193

With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT's performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (scienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT (Zhang et al., 2023) on the scienceQA test set. Our code is publicly available at https://***/Zoeyyao27/Graphof-Thought. © 2024 Association for Computational Linguistics.

关键词： Rats

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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 dep.oying 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 dep.ndency 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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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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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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Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning 38

Making Offline RL Online: Collaborative World Models for Off...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Qi Yang, Junming Wang, Yunbo Jin, Xin Zeng, Wenjun Yang, Xiaokang MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Ningbo Institute of Digital Twin Eastern Institute of Technology China School of Computer Science and Engineering Southeast University China

Training offline RL models using visual inputs poses two significant challenges, i.e., the overfitting problem in representation learning and the overestimation bias for expected future rewards. Recent work has attempted to alleviate the overestimation bias by encouraging conservative behaviors. This paper, in contrast, tries to build more flexible constraints for value estimation without impeding the exploration of potential advantages. The key idea is to leverage off-the-shelf RL simulators, which can be easily interacted with in an online manner, as the "test bed" for offline policies. To enable effective online-to-offline knowledge transfer, we introduce CoWorld, a model-based RL approach that mitigates cross-domain discrepancies in state and reward spaces. Experimental results demonstrate the effectiveness of CoWorld, outperforming existing RL approaches by large margins. © 2024 Neural information processing systems foundation. All rights reserved.

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