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检索条件"任意字段=Conference on empirical methods in natural language processing"

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On the In-context Generation of language Models

On the In-context Generation of Language Models

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Jiang, Zhongtao Zhang, Yuanzhe Luo, Kun Yuan, Xiaowei Zhao, Jun Liu, Kang The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798891761643

Large language models (LLMs) are found to have the ability of in-context generation (ICG): when they are fed with an in-context prompt concatenating a few somehow similar examples, they can implicitly recognize the pattern of them and then complete the prompt in the same pattern. ICG is curious, since language models are usually not explicitly trained in the same way as the in-context prompt, and the distribution of examples in the prompt differs from that of sequences in the pretrained corpora. This paper provides a systematic study of the ICG ability of language models, covering discussions about its source and influential factors, in the view of both theory and empirical experiments. Concretely, we first propose a plausible latent variable model to model the distribution of the pretrained corpora, and then formalize ICG as a problem of next topic prediction. With this framework, we can prove that the repetition nature of a few topics ensures the ICG ability on them theoretically. Then, we use this controllable pretrained distribution to generate several medium-scale synthetic datasets (token scale: 2.1B~3.9B) and experiment with different settings of Transformer architectures (parameter scale: 4M~234M). Our experimental results further offer insights into how the data and model architectures influence ICG. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

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Zero-Shot Sharpness-Aware Quantization for Pre-trained language Models

Zero-Shot Sharpness-Aware Quantization for Pre-trained Langu...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Zhu, Miaoxi Zhong, Qihuang Shen, Li Ding, Liang Liu, Juhua Du, Bo Tao, Dacheng Wuhan Univ Sch Comp Sci Natl Engn Res Ctr Multimedia Software Inst Artificial Intelligence Wuhan Peoples R China Wuhan Univ Hubei Key Lab Multimedia & Network Commun Engn Wuhan Peoples R China JD Explore Acad Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798891760608

Quantization is a promising approach for reducing memory overhead and accelerating inference, especially in large pre-trained language model (PLM) scenarios. While having no access to original training data due to security and privacy concerns has emerged the demand for zero-shot quantization. Most of the cuttingedge zero-shot quantization methods primarily (1) apply to computer vision tasks, and (2) neglect of overfitting problem in the generative adversarial learning process, leading to sub-optimal performance. Motivated by this, we propose a novel zero-shot sharpness-aware quantization (ZSAQ) framework for the zero-shot quantization of various PLMs. The key algorithm in solving ZSAQ is the SAM-SGA optimization, which aims to improve the quantization accuracy and model generalization via optimizing a minimax problem. We theoretically prove the convergence rate for the minimax optimization problem and this result can be applied to other nonconvex-PL minimax optimization frameworks. Extensive experiments on 11 tasks demonstrate that our method brings consistent and significant performance gains on both discriminative and generative PLMs, i.e., up to +6.98 average score. Furthermore, we empirically validate that our method can effectively improve the model generalization.

关键词： Optimization

来源：评论

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Please note that I'm just an AI: Analysis of Behavior Patterns of LLMs in (Non-)offensive Speech Identification

Please note that I'm just an AI: Analysis of Behavior Patter...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Dönmez, Esra Vu, Thang Falenska, Agnieszka Institute for Natural Language Processing University of Stuttgart Germany Interchange Forum for Reflecting on Intelligent Systems University of Stuttgart Germany

ISBN: (纸本)9798891761643

Offensive speech is highly prevalent on online *** trained on online data, Large language Models (LLMs) display undesirable behaviors, such as generating harmful text or failing to recognize *** these shortcomings, the models are becoming a part of our everyday lives by being used as tools for information search, content creation, writing assistance, and many ***, the research explores using LLMs in applications with immense social risk, such as late-life companions and online content *** the potential harms from LLMs in such applications, whether LLMs can reliably identify offensive speech and how they behave when they fail are open *** work addresses these questions by probing sixteen widely used LLMs and showing that most fail to identify (non-)offensive online *** experiments reveal undesirable behavior patterns in the context of offensive speech detection, such as erroneous response generation, over-reliance on profanity, and failure to recognize *** work highlights the need for extensive documentation of model reliability, particularly in terms of the ability to detect offensive language. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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UNICORN: A Unified Causal Video-Oriented language-Modeling Framework for Temporal Video-language Tasks

UNICORN: A Unified Causal Video-Oriented Language-Modeling F...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Xiong, Yuanhao Nie, Yixin Liu, Haotian Wang, Boxin Chen, Jun Jin, Rong Hsieh, Cho-Jui Torresani, Lorenzo Lei, Jie UCLA United States Meta United States University of Wisconsin-Madison United States UIUC United States

ISBN: (纸本)9798891761643

The great success of large language models has encouraged the development of large multimodal models, with a focus on image-language interaction. Despite promising results in various image-language downstream tasks, it is still challenging and unclear how to extend the capabilities of these models to the more complex video domain, especially when dealing with explicit temporal signals. To address the problem in existing large multimodal models, in this paper we adopt visual instruction tuning to build a unified causal video-oriented language modeling framework, named UNICORN. Specifically, we collect a comprehensive dataset under the instruction-following format, and instruction-tune the model accordingly. Experimental results demonstrate that without customized training objectives and intensive pre-training, UNICORN can achieve comparable or better performance on established temporal video-language tasks including moment retrieval, video paragraph captioning and dense video captioning. Moreover, the instruction-tuned model can be used to automatically annotate internet videos with temporally-aligned captions. Compared to commonly used ASR captions, we show that training on our generated captions improves the performance of video-language models on both zero-shot and fine-tuning settings. Source code can be found at https://***/xyh97/UNICORN. © 2024 Association for Computational Linguistics.

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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 ...

引用

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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PromptReps: Prompting Large language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval

PromptReps: Prompting Large Language Models to Generate Dens...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Zhuang, Shengyao Ma, Xueguang Koopman, Bevan Lin, Jimmy Zuccon, Guido CSIRO Australia The University of Queensland Australia University of Waterloo Canada

ISBN: (纸本)9798891761643

Utilizing large language models (LLMs) for zero-shot document ranking is done in one of two ways: (1) prompt-based re-ranking methods, which require no further training but are only feasible for re-ranking a handful of candidate documents due to computational costs;and (2) unsupervised contrastive trained dense retrieval methods, which can retrieve relevant documents from the entire corpus but require a large amount of paired text data for contrastive training. In this paper, we propose PromptReps, which combines the advantages of both categories: no need for training and the ability to retrieve from the whole corpus. Our method only requires prompts to guide an LLM to generate query and document representations for effective document retrieval. Specifically, we prompt the LLMs to represent a given text using a single word, and then use the last token's hidden states and the corresponding logits associated with the prediction of the next token to construct a hybrid document retrieval system. The retrieval system harnesses both dense text embedding and sparse bag-of-words representations given by the LLM. Our experimental evaluation on the MSMARCO, TREC deep learning and BEIR zero-shot document retrieval datasets illustrates that this simple prompt-based LLM retrieval method can achieve a similar or higher retrieval effectiveness than state-of-the-art LLM embedding methods that are trained with large amounts of unsupervised data, especially when using a larger LLM. © 2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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Universal Vulnerabilities in Large language Models: Backdoor Attacks for In-context Learning

Universal Vulnerabilities in Large Language Models: Backdoor...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Zhao, Shuai Jia, Meihuizi Tuan, Luu Anh Pan, Fengjun Wen, Jinming Nanyang Technological University Singapore Guangzhou University Guangzhou China Beijing Institute of Technology Beijing China

ISBN: (纸本)9798891761643

In-context learning, a paradigm bridging the gap between pre-training and fine-tuning, has demonstrated high efficacy in several NLP tasks, especially in few-shot settings. Despite being widely applied, in-context learning is vulnerable to malicious attacks. In this work, we raise security concerns regarding this paradigm. Our studies demonstrate that an attacker can manipulate the behavior of large language models by poisoning the demonstration context, without the need for fine-tuning the model. Specifically, we design a new backdoor attack method, named ICLAttack, to target large language models based on in-context learning. Our method encompasses two types of attacks: poisoning demonstration examples and poisoning demonstration prompts, which can make models behave in alignment with predefined intentions. ICLAttack does not require additional fine-tuning to implant a backdoor, thus preserving the model's generality. Furthermore, the poisoned examples are correctly labeled, enhancing the natural stealth of our attack method. Extensive experimental results across several language models, ranging in size from 1.3B to 180B parameters, demonstrate the effectiveness of our attack method, exemplified by a high average attack success rate of 95.0% across the three datasets on OPT models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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DEPN: Detecting and Editing Privacy Neurons in Pretrained language Models

DEPN: Detecting and Editing Privacy Neurons in Pretrained La...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Wu, Xinwei Li, Junzhuo Xu, Minghui Dong, Weilong Wu, Shuangzhi Bian, Chao Xiong, Deyi Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Tianjin Univ Sch New Media & Commun Tianjin Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China ByteDance Lark AI Beijing Peoples R China

ISBN: (纸本)9798891760608

Large language models pretrained on a huge amount of data capture rich knowledge and information in the training data. The ability of data memorization and regurgitation in pre-trained language models, revealed in previous studies, brings the risk of data leakage. In order to effectively reduce these risks, we propose a framework DEPN to Detect and Edit Privacy Neurons in pretrained language models, partially inspired by knowledge neurons and model editing. In DEPN, we introduce a novel method, termed as privacy neuron detector, to locate neurons associated with private information, and then edit these detected privacy neurons by setting their activations to zero. Furthermore, we propose a privacy neuron aggregator dememorize private information in a batch processing manner. Experimental results show that our method can significantly and efficiently reduce the exposure of private data leakage without deteriorating the performance of the model. Additionally, we empirically demonstrate the relationship between model memorization and privacy neurons, from multiple perspectives, including model size, training time, prompts, privacy neuron distribution, illustrating the robustness of our approach.

关键词： Neurons

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Compressing Context to Enhance Inference Efficiency of Large language Models

Compressing Context to Enhance Inference Efficiency of Large...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Li, Yucheng Dong, Bo Guerin, Frank Lin, Chenghua Univ Surrey Dept Comp Sci Guildford Surrey England Univ Manchester Dept Comp Sci Manchester Lancs England Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England

ISBN: (纸本)9798891760608

Large language models (LLMs) achieved remarkable performance across various tasks. However, they face challenges in managing long documents and extended conversations, due to significantly increased computational requirements, both in memory and inference time, and potential context truncation when the input exceeds the LLM's fixed context length. This paper proposes a method called Selective Context that enhances the inference efficiency of LLMs by identifying and pruning redundancy in the input context to make the input more compact. We test our approach using common data sources requiring long context processing: arXiv papers, news articles, and long conversations, on tasks of summarisation, question answering, and response generation. Experimental results show that Selective Context significantly reduces memory cost and decreases generation latency while maintaining comparable performance compared to that achieved when full context is used. Specifically, we achieve a 50% reduction in context cost, resulting in a 36% reduction in inference memory usage and a 32% reduction in inference time, while observing only a minor drop of .023 in BERTscore and .038 in faithfulness on four downstream applications, indicating that our method strikes a good balance between efficiency and performance. Code and data are available at https://***/liyucheng09/Selective_Context.

关键词： Efficiency

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Discovering Biases in Information Retrieval Models Using Relevance Thesaurus as Global Explanation

Discovering Biases in Information Retrieval Models Using Rel...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Kim, Youngwoo Rahimi, Razieh Allan, James University of Massachusetts Amherst United States

ISBN: (纸本)9798891761643

Most efforts in interpreting neural relevance models have focused on local explanations, which explain the relevance of a document to a query but are not useful in predicting the model's behavior on unseen query-document *** propose a novel method to globally explain neural relevance models by constructing a "relevance thesaurus" containing semantically relevant query and document term *** thesaurus is used to augment lexical matching models such as BM25 to approximate the neural model's *** method involves training a neural relevance model to score the relevance of partial query and document segments, which is then used to identify relevant terms across the vocabulary *** evaluate the obtained thesaurus explanation based on ranking effectiveness and fidelity to the target neural ranking ***, our thesaurus reveals the existence of brand name bias in ranking models, demonstrating one advantage of our explanation method. © 2024 Association for Computational Linguistics.

关键词： Structured Query language

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