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62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Liang, Xun Song, Shichao Niu, Simin Li, Zhiyu Xiong, Feiyu Tang, Bo Wang, Yezhaohui He, Dawei Cheng, Peng Wang, Zhonghao Deng, Haiying School of Information Renmin University of China Beijing China Institute for Advanced Algorithms Research Shanghai China State Key Laboratory of Media Convergence Production Technology and Systems Beijing China

ISBN: (纸本)9798891760943

Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts. To assess the reliability of LLMs, numerous initiatives have developed benchmark evaluations for hallucination phenomena. However, they often employ constrained generation techniques to produce the evaluation dataset due to cost and time limitations. For instance, this may involve employing directed hallucination induction or deliberately modifying authentic text to generate hallucinations. These are not congruent with the unrestricted text generation demanded by real-world applications. Furthermore, a well-established Chinese-language dataset dedicated to the evaluation of hallucinations is presently lacking. Consequently, we have developed an Unconstrained Hallucination Generation Evaluation (UHGEval) benchmark, containing hallucinations generated by LLMs with minimal restrictions. Concurrently, we have established a comprehensive benchmark evaluation framework to aid subsequent researchers in undertaking scalable and reproducible experiments. We have also evaluated prominent Chinese LLMs and the GPT series models to derive insights regarding hallucination. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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When Sparse Graph Representation Learning Falls into Domain Shift: Feature Augmentation for Cross-Domain Graph Meta-Learning

When Sparse Graph Representation Learning Falls into Domain ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Niu, Simin Liang, Xun Zhang, Sensen Li, Zhiyu Zhang, Xuan Bo, Wu Wang, Hanyu Song, Shichao Wang, Mengwei Yang, Jiawei School of Information Renmin University of China Beijing China Institute for Advanced Algorithms Research Shanghai China Guanghua School of Management Peking University Beijing China Xiangjiang Laboratory Central South University Hunan China

ISBN: (纸本)9798350368741

Graph Meta-learning methods have improved the performance of few-shot node classification by means of applying meta-learning to the data in non-Euclidean domains. However, most works focus on adopting a single domain, ignoring the fact that tasks in various domains may be distinct, which can cause overfitting problems and thus limit generalizability. To tackle this challenge, we propose a novel Graph Meta-learning framework called Feature-Enhanced Cross-domain Graph Meta-learning that consists of two crucial modules: 1) A feature information extraction module that aims to capture discriminative node importance and simulate various node feature distributions under distinct domains;2) A heterogeneous graph encoder module that leverages the enhanced node features and topological information to generate task-specific node embeddings with simple fine-tuning. Moreover, we meta-learn the parameters involved to ensure the generalizability in the unseen domains. Results show that our method markedly outperforms the existing state-of-the-art methods. © 2025 IEEE.

关键词： Domain Generalization Few-shot Learning Heterogeneous Graph Neural Networks Meta-learning

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Retrieval-Augmented Multilingual Citation Generation

Retrieval-Augmented Multilingual Citation Generation

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Xun Niu, Simin Zhang, Sensen Li, Zhiyu Zhang, Xuan Wu, Bo Xiong, Feiyu Tang, Bo Wang, Hanyu Song, Shichao Wang, Mengwei Yang, Jiawei School of Information Renmin University of China Beijing China Institute for Advanced Algorithms Research Shanghai China Guanghua School of Management Peking University Beijing China Xiangjiang Laboratory Central South University Hunan China

ISBN: (纸本)9798350368741

Retrieval-augmented citation generation (RACG) helps users trust the large language model output by retrieving evidence from reliable sources. However, most current RACG research focuses on single-language tasks, particularly in English, and overlooks the need for cross-lingual evidence retrieval and utilization in real-world applications. To address this issue, we introduce a plug-and-play Retrieval-Augmented Multilingual Citation Generation method (RAMCG) which uses a multilingual retriever to identify relevant evidence from a multilingual knowledge base. The evidence is then combined with the query and processed by a multilingual citation generator. The result is citations that are both accurate and comprehensive. Experiments show that RAMCG outperforms baseline methods in multilingual citation generation and is well-suited for practical use. © 2025 IEEE.

关键词： Applications of Generative AI Citation Generation Retrieval-Augmented Generation

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Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing 38

Initialization is Critical to Whether Transformers Fit Compo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Zhongwang Lin, Pengxiao Wang, Zhiwei Zhang, Yaoyu Xu, Zhi-Qin John Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University China School of Mathematical Sciences Shanghai Jiao Tong University China Key Laboratory of Marine Intelligent Equipment and System Ministry of Education China Shanghai Seres Information Technology Co. Ltd. Shanghai China Center for LLM Institute for Advanced Algorithms Research Shanghai China

Transformers have shown impressive capabilities across various tasks, but their performance on compositional problems remains a topic of debate. In this work, we investigate the mechanisms of how transformers behave on unseen compositional tasks. We discover that the parameter initialization scale plays a critical role in determining whether the model learns inferential (reasoning-based) solutions, which capture the underlying compositional primitives, or symmetric (memory-based) solutions, which simply memorize mappings without understanding the compositional structure. By analyzing the information flow and vector representations within the model, we reveal the distinct mechanisms underlying these solution types. We further find that inferential (reasoning-based) solutions exhibit low complexity bias, which we hypothesize is a key factor enabling them to learn individual mappings for single anchors. We validate our conclusions on various real-world datasets. Our findings provide valuable insights into the role of initialization scale in tuning the reasoning and memorizing ability and we propose the initialization rate γ to be a convenient tunable hyper-parameter in common deep learning frameworks, where 1/dγin is the standard deviation of parameters of the layer with din input neurons. © 2024 Neural information processing systems foundation. All rights reserved.

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Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy

arXiv

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arXiv 2024年

作者： Zhu, Yu Sun, Chuxiong Yang, Wenfei Wei, Wenqiang Tang, Bo Zhang, Tianzhu Li, Zhiyu Zhang, Shifeng Xiong, Feiyu Hu, Jie Yang, Mingchuan University of Science and Technology of China Hefei China Deep Space Exploration Laboratory China Institute for Advanced Algorithms Research Shanghai China Research Institute of China Telecom China Sangfor Technologies Inc. China

Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values. However, existing RLHF methods require a high computational cost, one main reason being that RLHF assigns both the generation and alignment tasks to the LLM simultaneously. In this paper, we introduce Proxy-RLHF, which decouples the generation and alignment processes of LLMs, achieving alignment with human values at a much lower computational cost. We start with a novel Markov Decision Process (MDP) designed for the alignment process and employ Reinforcement Learning (RL) to train a streamlined proxy model that oversees the token generation of the LLM, without altering the LLM itself. Experiments show that our method achieves a comparable level of alignment with only 1% of the training parameters of other methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism

arXiv

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arXiv 2024年

作者： Li, Miao Chen, Ming-Bin Tang, Bo Hou, Shengbin Wang, Pengyu Deng, Haiying Li, Zhiyu Xiong, Feiyu Mao, Keming Cheng, Peng Luo, Yi School of Computing and Information Systems The University of Melbourne Australia Institute for Advanced Algorithms Research China Northeastern University China State Key Laboratory of Media Convergence Production Technology and Systems China

We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. Our constructed benchmark dataset is focused on four facets of writing proficiency and six facets of safety adherence, and it comprises manually and carefully designed 1,267 test samples in the types of multiple choice questions and short answer questions for five editorial tasks in 24 news domains. To measure performances, we propose different GPT-4 based automatic evaluation protocols to assess LLM generations for short answer questions in terms of writing proficiency and safety adherence, and both are validated by the high correlations with human evaluations. Based on the systematic evaluation framework, we conduct a comprehensive analysis of eleven popular LLMs which can handle Chinese. The experimental results highlight GPT-4 and ERNIE Bot as top performers, yet reveal a relative deficiency in journalistic safety adherence in creative writing tasks. Our findings also underscore the need for enhanced ethical guidance in machine-generated journalistic content, marking a step forward in aligning LLMs with journalistic standards and safety considerations. The evaluation framework and experimental results are expected to provide an in-depth understanding of the editorial capabilities of LLMs and speed up the development of LLMs in journalism.1 © 2024, CC BY.

关键词： Statistical tests

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When Sparse Graph Representation Learning Falls into Domain Shift: Feature Augmentation for Cross-Domain Graph Meta-Learning

When Sparse Graph Representation Learning Falls into Domain ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Simin Niu Xun Liang Sensen Zhang Zhiyu Li Xuan Zhang Wu Bo Hanyu Wang Shichao Song Mengwei Wang Jiawei Yang School of Information Renmin University of China Beijing China Institute for Advanced Algorithms Research Shanghai China Guanghua School of Management Peking University Beijing China Xiangjiang Laboratory Central South University Hunan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Meta-learning methods have improved the performance of few-shot node classification by means of applying meta-learning to the data in non-Euclidean domains. However, most works focus on adopting a single domain, ignoring the fact that tasks in various domains may be distinct, which can cause overfitting problems and thus limit generalizability. To tackle this challenge, we propose a novel Graph Meta-learning framework called Feature-Enhanced Cross-domain Graph Meta-learning that consists of two crucial modules: 1) A feature information extraction module that aims to capture discriminative node importance and simulate various node feature distributions under distinct domains; 2) A heterogeneous graph encoder module that leverages the enhanced node features and topological information to generate task-specific node embeddings with simple fine-tuning. Moreover, we meta-learn the parameters involved to ensure the generalizability in the unseen domains. Results show that our method markedly outperforms the existing state-of-the-art methods.

关键词： Metalearning Representation learning Signal processing Feature extraction Information retrieval Acoustics Data mining Speech processing Overfitting

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Retrieval-Augmented Multilingual Citation Generation

Retrieval-Augmented Multilingual Citation Generation

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xun Liang Simin Niu Sensen Zhang Zhiyu Li Xuan Zhang Bo Wu Feiyu Xiong Bo Tang Hanyu Wang Shichao Song Mengwei Wang Jiawei Yang School of Information Renmin University of China Beijing China Institute for Advanced Algorithms Research Shanghai China Guanghua School of Management Peking University Beijing China Xiangjiang Laboratory Central South University Hunan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Measurement Generative AI Large language models Knowledge based systems Signal processing Generators Acoustics Multilingual Reliability Speech processing

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Overall and Delay Complexity of the CLIQUES and Bron-Kerbosch algorithms 15th

Overall and Delay Complexity of the CLIQUES and Bron-Kerbosc...

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15th International Conference on algorithms and Computation, WALCOM 2021

作者： Conte, Alessio Tomita, Etsuji University of Pisa Pisa Italy The Advanced Algorithms Research Laboratory The University of Electro-Communications Chofugaoka 1–5–1 Chofu Tokyo182–8585 Japan

ISBN: (纸本)9783030682101

We revisit the maximal clique enumeration algorithm cliques by Tomita et al. that appeared in Theoretical Computer Science 2006. It is known to work in O(3n/3) -time in the worst-case for an n-vertex graph. In this paper, we extend the time-complexity analysis with respect to the maximum size and the number of maximal cliques, and to its delay, solving issues that were left as open problems since the original paper. In particular, we prove that cliques does not have polynomial delay, unless P= NP, and that this remains true for any possible pivoting strategy, for both cliques and Bron-Kerbosch. As these algorithms are widely used and regarded as fast "in practice", we are interested in observing their practical behavior: we run an evaluation of cliques and three Bron-Kerbosch variants on over 130 real-world and synthetic graphs, and observe how their performance seems far from its theoretical worst-case behavior in terms of both total time and delay. © 2021, Springer Nature Switzerland AG.

关键词： Computers

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UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation

arXiv

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arXiv 2023年

Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts. To assess the reliability of LLMs, numerous initiatives have developed benchmark evaluations for hallucination phenomena. However, they often employ constrained generation techniques to produce the evaluation dataset due to cost and time limitations. For instance, this may involve employing directed hallucination induction or deliberately modifying authentic text to generate hallucinations. These are not congruent with the unrestricted text generation demanded by real-world applications. Furthermore, a well-established Chinese-language dataset dedicated to the evaluation of hallucinations is presently lacking. Consequently, we have developed an Unconstrained Hallucination Generation Evaluation (UHGEval) benchmark, containing hallucinations generated by LLMs with minimal restrictions1. Concurrently, we have established a comprehensive benchmark evaluation framework to aid subsequent researchers in undertaking scalable and reproducible experiments. We have also evaluated prominent Chinese LLMs and the GPT series models to derive insights regarding hallucination. Copyright © 2023, The Authors. All rights reserved.

关键词： Petroleum reservoir evaluation

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