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

作者： Ye, Junjie Wu, Yilong Gao, Songyang Huang, Caishuang Li, Sixian Li, Guanyu Fan, Xiaoran Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9798891761643

Tool learning has generated widespread interest as a vital means of interaction between Large Language Models (LLMs) and the physical world. Current research predominantly emphasizes LLMs' capacity to utilize tools in well-structured environments while overlooking their stability when confronted with the inevitable noise of the real world. To bridge this gap, we introduce RoTBench, a multi-level benchmark for evaluating the robustness of LLMs in tool learning. Specifically, we establish five external environments, each featuring varying levels of noise (i.e., Clean, Slight, Medium, Heavy, and Union), providing an in-depth analysis of the model's resilience across three critical phases: tool selection, parameter identification, and content filling. Experiments involving six widely-used models underscore the urgent necessity for enhancing the robustness of LLMs in tool learning. For instance, the performance of GPT-4 even drops significantly from 80.00 to 58.10 when there is no substantial change in manual accuracy. More surprisingly, the noise correction capability inherent in the GPT family paradoxically impedes its adaptability in the face of mild noise. In light of these findings, we propose RoTTuning, a strategy that enriches the diversity of training environments to bolster the robustness of LLMs in tool learning. The code and data are available at https://***/Junjie-Ye/RoTBench. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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PDF-to-Tree: Parsing PDF Text Blocks into a Tree

PDF-to-Tree: Parsing PDF Text Blocks into a Tree

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

作者： Zhang, Yue Zhang, Zhihao Lai, Wenbin Zhang, Chong Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9798891761681

In many PDF documents, the reading order of text blocks is missing, which can hinder machine understanding of the document's content. Existing works try to extract one universal reading order for a PDF file. However, applications, like Retrieval Augmented Generation (RAG), require breaking long articles into sections, subsections and table cells for better indexing. For this reason, this paper introduces a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure. Since a PDF may contain thousands of text blocks, this paper proposes a transition-based parser that uses a greedy strategy to build the tree structure. Compared to the parser for plain text, we also use multi-modal features to encode the parser state. Experiments show that our approach achieves an accuracy of 93.93%, surpassing the performance of baseline methods by an improvement of 6.72%. The dataset is public available at https://***/yuezh000/PDFto-Tree. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A design- for-diagnosis technique for diagnosing both scan chain faults and combinational circuit faults

A design- for-diagnosis technique for diagnosing both scan c...

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2008 Asia and South Pacific Design Automation Conference, ASP-DAC

作者： Wang, Fei Hu, Yu Li, Huawei Li, Xiaowei Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Science Beijing China Graduate University Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424419227

The amount of die area consumed by scan chains and scan control circuit can range from 15%∼30%, and scan chain failures account for almost 50% of chip failures. As the conventional diagnosis process usually runs on the faulty free scan chain, scan chain faults may disable the diagnostic process, leaving large failure area to time-consuming failure analysis. In this paper, a design-for-diagnosis (DFD) technique is proposed to diagnose faulty scan chains precisely and efficiently, moreover, with the assistant of the proposed technique, the conventional logic diagnostic process can be carried on with faulty scan chains. The proposed approach is entirely compatible with conventional scan-based design. Previously proposed software-based diagnostic methods for conventional scan designs can still be applied to our design. Experiments on ISCAS'89 benchmark circuits are conducted to demonstrate the efficiency of the proposed DFD technique. ©2008 IEEE.

关键词： Chains

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A trimaran based framework for exploring the design space of VLIW ASIPs with coarse grain functional units

A trimaran based framework for exploring the design space of...

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15th International Symposium on System Synthesis

作者： Middha, Bhuvan Raj, Varun Gangwar, Anup Kumar, Anshul Balakrishnan, M. Ienne, Paolo Department of Computer Science Indian Institute of Technology Delhi India Processor Architecture Laboratory Swiss Fed. Inst. Technol. Laussane Switzerland

It is widely accepted that use of an Application Specific Instruction Set Processor (ASIP) in an embedded system can provide a solution which is much more flexible than ASICs and much more efficient than standard processors in terms of performance and power consumption. However a lack of an acceptable design methodology and supporting tools for ASIPs limits their use even today. We present in this paper a methodology for design space exploration of high performance VLIW ASIPs by modeling Application Specific Functional Units in Trimaran Compiler Infrastructure. To demonstrate the effectiveness of our strategy we consider two important applications FFT and Kalman Filter and perform compute intensive operations in these applications via special Functional Units. The results we obtain are very promising with up to 2× speed improvement.

关键词： Program processors

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Improving Discriminative Capability of Reward Models in RLHF Using Contrastive Learning

Improving Discriminative Capability of Reward Models in RLHF...

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

作者： Chen, Lu Zheng, Rui Wang, Binghai Jin, Senjie Huang, Caishuang Ye, Junjie Zhang, Zhihao Zhou, Yuhao Xi, Zhiheng Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9798891761643

Reinforcement Learning from Human Feedback (RLHF) is a crucial approach to aligning language models with human values and intentions. A fundamental challenge in this method lies in ensuring that the reward model accurately understands and evaluates human preferences. Current methods rely on ranking losses to teach the reward model to assess preferences, but they are susceptible to noise and ambiguous data, often failing to deeply understand human intentions. To address this issue, we introduce contrastive learning into the reward modeling process. In addition to supervised ranking loss, we introduce an unsupervised contrastive loss to enable the reward model to fully capture the distinctions in contrastive data. Experimental results demonstrate that the proposed contrastive learning-based reward modeling method effectively enhances the generalization of the reward model, stabilizes the reinforcement learning training process, and improves the final alignment with human preferences. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration

LONGAGENT: Achieving Question Answering for 128k-Token-Long ...

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

作者： Zhao, Jun Zu, Can Xu, Hao Lu, Yi He, Wei Ding, Yiwen Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Institute of Modern Languages and Linguistics Fudan University China

ISBN: (纸本)9798891761643

Large language models (LLMs) have achieved tremendous success in understanding language and processing text. However, question-answering (QA) on lengthy documents faces challenges of resource constraints and a high propensity for errors, even for the most advanced models such as GPT-4 and Claude2. In this paper, we introduce LONGAGENT, a multi-agent collaboration method that enables efficient and effective QA over 128k-token-long documents. LONGAGENT adopts a divide- and-conquer strategy, breaking down lengthy documents into shorter, more manageable text chunks. A leader agent comprehends the user's query and organizes the member agents to read their assigned chunks, reasoning a final answer through multiple rounds of discussion. Due to members' hallucinations, it's difficult to guarantee that every response provided by each member is accurate. To address this, we develop an inter-member communication mechanism that facilitates information sharing, allowing for the detection and mitigation of hallucinatory responses. Experimental results show that a LLaMA-2 7B driven by LONGAGENT can effectively support QA over 128k-token documents, achieving 16.42% and 1.63% accuracy gains over GPT-4 on single-hop and multi-hop QA settings, respectively. © 2024 Association for Computational Linguistics.

关键词： Question answering

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LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases 29

LFKQG: A Controlled Generation Framework with Local Fine-tun...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Fei, Zichu Zhou, Xin Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan Unviersity China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China

Question generation over knowledge bases (KBQG) aims to generate natural questions about a subgraph that can be answered by a given answer entity. Existing KBQG models still face two main challenges: (1) Most models often focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph. (2) There are a large number of out-of-vocabulary (OOV) predicates in real-world scenarios, which are hard to adapt for most KBQG models. To address these challenges, we propose LFKQG, a controlled generation framework for Question Generation over Knowledge Bases. (1) LFKQG employs a simple controlled generation method to generate the questions containing the critical entities in the subgraph, ensuring the question is relevant to the whole subgraph. (2) We propose an optimization strategy called local fine-tuning, which makes good use of the rich information hidden in the pre-trained model to improve the ability of the model to adapt the OOV predicates. Extensive experiments show that our method outperforms existing methods greatly on three widely-used benchmark datasets SimpleQuestion, PathQuestions, and WebQuestions . © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

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HCMonitor: An accurate measurement system for high concurrent network services

HCMonitor: An accurate measurement system for high concurren...

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作者： Song, Hui Zhang, Wenli Liu, Ke Shen, Yifan Chen, Mingyu State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Frontier Research Center Peng Cheng Laboratory Shenzhen China

This article aims to enhance the monitoring accuracy of high concurrent network services. As modern network services grow rapidly in data centers, tail latency has become one of the most crucial deciding factors on user experience. Latency measurement and anomaly detection are essential in evaluating service performance. Existing monitoring tools can be divided into two categories according to estimation methods. First, approaches based on sample traffic sample network packets to unburden the measurement. Second, approaches based on full traffic like wrk, analyze all of the packets from the kernel network stack and load the client-side overhead into response delay. Therefore, we propose a high-performance monitor system named HCMonitor, which computes the server-side response latency and the round-trip time of per-request. It can afford full traffic monitoring on the basis of userspace, "zero copy" and pipeline. By switch mirroring, the measured latency eliminates the kernel network stack overhead and the queuing delay of the client-side. Such measurement results in improved accuracy, online analysis, anomaly detection, real-time display and transparent to network services. Our evaluations show HCMonitor obtains a higher throughput compared with tcpdump by over 200 times. Compared with wrk, the tail latency accuracy shows an increase by up to 72%–76% in high concurrent networks. © 2021 John Wiley & Sons Ltd.

关键词： Anomaly detection

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A Power and Area Optimization Approach of Mixed Polarity Reed-Muller Expression for Incompletely Specified Boolean Functions

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Journal of computer science & Technology 2017年第2期32卷 297-311页

作者： Zhen-Xue He Li-Min Xiao Li Ruan Fei Gu Zhi-Sheng Huo Guang-Jun Qin Ming-Fa Zhu F Long-Bing Zhang Rui Liu Xiang Wang State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China School of Electronic and Information Engineering Beihang University Beijing 100191 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China National Engineering Research Center for Science and Technology Resources Sharing Service Beihang University Beijing 100191 China

The power and area optimization of Reed-Muller （RM） circuits has been widely concerned. However, almost none of the exiting power and area optimization approaches can obtain all the Pareto optimal solutions of the original problem and are efficient enough. Moreover, they have not considered the don＇t care terms, which makes the circuit performance unable to be further optimized. In this paper, we propose a power and area optimization approach of mixed polarity RM expression （MPRM） for incompletely specified Boolean functions based on Non-Dominated Sorting Genetic Algorithm II （NSGA-II）. Firstly, the incompletely specified Boolean function is transformed into zero polarity incompletely specified MPRM （ISMPRM） by using a novel ISMPRM acquisition algorithm. Secondly, the polarity and allocation of don＇t care terms of ISMPRM is encoded as chromosome. Lastly, the Pareto optimal solutions are obtained by using NSGA-II, in which MPRM corresponding to the given chromosome is obtained by using a chromosome conversion algorithm. The results on incompletely specified Boolean functions and MCNC benchmark circuits show that a significant power and area improvement can be made compared with the existing power and area optimization approaches of RM circuits.

关键词： power and area optimization Reed-Muller （RM） circuit Pareto optimal solution don＇t care term chromosomeconversion

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Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER 29

Read Extensively, Focus Smartly: A Cross-document Semantic E...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Zhao, Jun Zhao, Xin Zhan, Wenyu Gui, Tao Zhang, Qi Qiao, Liang Cheng, Zhanzhan Pu, Shiliang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University China Hikvision Research Institute Shanghai China

The introduction of multimodal information and pretraining technique significantly improves entity recognition from visually-rich documents. However, most of the existing methods pay unnecessary attention to irrelevant regions of the current document while ignoring the potentially valuable information in related documents. To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document. 2) To further enrich the entity-related context, we propose a cross-document information awareness technique, which enables the model to collect more evidence across documents to assist in prediction. The experimental results on two documents understanding benchmarks covering eight languages demonstrate that our method outperforms the SOTA methods. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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