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

共 15363 条记录，以下是921-930 订阅

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Automatic Transcription of Handwritten Old Occitan language

Automatic Transcription of Handwritten Old Occitan Language

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Arias, Esteban Garces Pai, Vallari Schoeffel, Matthias Heumann, Christian Assenmacher, Matthias LMU Dept Stat Munich Germany Bavarian Acad Sci BAdW Munich Germany LMU Munich Ctr Machine Learning MCML Munich Germany

ISBN: (纸本)9798891760608

While existing neural network-based approaches have shown promising results in Handwritten Text Recognition (HTR) for high-resource languages and standardized/machinewritten text, their application to low-resource languages often presents challenges, resulting in reduced effectiveness. In this paper, we propose an innovative HTR approach that leverages the Transformer architecture for recognizing handwritten Old Occitan language. Given the limited availability of data, which comprises only word pairs of graphical variants and lemmas, we develop and rely on elaborate data augmentation techniques for both text and image data. Our model combines a custom-trained Swin image encoder with a BERT text decoder, which we pre-train using a large-scale augmented synthetic data set and fine-tune on the small human-labeled data set. Experimental results reveal that our approach surpasses the performance of current state-of-the-art models for Old Occitan HTR, including open-source Transformer-based models such as a fine-tuned TrOCR and commercial applications like Google Cloud Vision. To nurture further research and development, we make our models, data sets, and code publicly available: https://***/misoda

关键词： Character recognition

来源：评论

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MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation

MADNet: Maximizing Addressee Deduction Expectation for Multi...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Gu, Jia-Chen Tan, Chao-Hong Chu, Caiyuan Ling, Zhen-Hua Tao, Chongyang Liu, Quan Liu, Cong Univ Sci & Technol China Natl Engn Res Ctr Speech & Language Informat Proc Hefei Peoples R China iFLYTEK Res Hefei Peoples R China Peking Univ Beijing Peoples R China State Key Lab Cognit Intelligence Beijing Peoples R China

ISBN: (纸本)9798891760608

Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods rely heavily on the necessary addressee labels and can only be applied to an ideal setting where each utterance must be tagged with an "@" or other equivalent addressee label. To study the scarcity of addressee labels which is a common issue in MPCs, we propose MADNet that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. Given an MPC with a few addressee labels missing, existing methods fail to build a consecutively connected conversation graph, but only a few separate conversation fragments instead. To ensure message passing between these conversation fragments, four additional types of latent edges are designed to complete a fully-connected graph. Besides, to optimize the edge-type-dependent message passing for those utterances without addressee labels, an Expectation-Maximization-based method that iteratively generates silver addressee labels (E step), and optimizes the quality of generated responses (M step), is designed. Experimental results on two Ubuntu IRC channel benchmarks show that MADNet outperforms various baseline models on the task of MPC generation, especially under the more common and challenging setting where part of addressee labels are missing.

关键词： Message passing

来源：评论

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Appraising the Potential Uses and Harms of Large language Models for Medical Systematic Reviews

Appraising the Potential Uses and Harms of Large Language Mo...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Yun, Hye Sun Marshall, Iain J. Trikalinos, Thomas A. Wallace, Byron C. Northeastern Univ Boston MA 02115 USA Kings Coll London London England Brown Univ Providence RI 02912 USA

ISBN: (纸本)9798891760608

Medical systematic reviews play a vital role in healthcare decision making and policy. However, their production is time-consuming, limiting the availability of high-quality and up-to-date evidence summaries. Recent advances in large language models (LLMs) offer the potential to automatically generate literature reviews on demand, addressing this issue. However, LLMs sometimes generate inaccurate (and potentially misleading) texts by "hallucination" or omission. In healthcare, this can make LLMs unusable at best and dangerous at worst. We conducted 16 interviews with international systematic review experts to characterize the perceived utility and risks of LLMs in the specific context of medical evidence reviews. Experts indicated that LLMs can assist in the writing process by drafting summaries, generating templates, distilling information, and crosschecking information. But they also raised concerns regarding confidently composed but inaccurate LLM outputs and other potential downstream harms, including decreased accountability and proliferation of low-quality reviews. Informed by this qualitative analysis, we identify criteria for rigorous evaluation of biomedical LLMs aligned with domain expert views.

关键词： Decision making

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Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large language Models

Editing the Mind of Giants: An In-Depth Exploration of Pitfa...

引用

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

作者： Hsueh, Cheng-Hsun Huang, Paul Kuo-Ming Lin, Tzu-Han Liao, Che-Wei Fang, Hung-Chieh Huang, Chao-Wei Chen, Yun-Nung National Taiwan University Taipei Taiwan

ISBN: (纸本)9798891761681

Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of ***, recent studies have identified side effects, such as knowledge distortion and the deterioration of general abilities, that have emerged after *** these findings, evaluating the pitfalls of knowledge editing often relies on inconsistent metrics and benchmarks, lacking a uniform *** response, this survey presents a comprehensive study of these side effects, providing a unified perspective on the challenges of knowledge editing in LLMs by conducting experiments with consistent metrics and ***, we review related works and outline potential research directions to address these *** survey highlights the limitations of current knowledge editing methods, emphasizing the need for a deeper understanding of the inner knowledge structures of LLMs and improved knowledge editing *** foster future research, we have released the complementary materials publicly. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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QUITE: Quantifying Uncertainty in natural language Text in Bayesian Reasoning Scenarios

QUITE: Quantifying Uncertainty in Natural Language Text in B...

引用

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

作者： Schrader, Timo Pierre Lange, Lukas Razniewski, Simon Friedrich, Annemarie Bosch Center for Artificial Intelligence Renningen Germany University of Augsburg Augsburg Germany ScaDS.AI TU Dresden Dresden Germany

ISBN: (纸本)9798891761643

Reasoning is key to many decision making processes. It requires consolidating a set of rule-like premises that are often associated with degrees of uncertainty and observations to draw conclusions. In this work, we address both the case where premises are specified as numeric probabilistic rules and situations in which humans state their estimates using words expressing degrees of certainty. Existing probabilistic reasoning datasets simplify the task, e.g., by requiring the model to only rank textual alternatives, by including only binary random variables, or by making use of a limited set of templates that result in less varied text. In this work, we present QUITE, a question answering dataset of real-world Bayesian reasoning scenarios with categorical random variables and complex relationships. QUITE provides high-quality natural language verbalizations of premises together with evidence statements, and expects the answer to a question in the form of an estimated probability. We conduct an extensive set of experiments, finding that logic-based models outperform out-of-the-box large language models on all reasoning types (causal, evidential, and explaining-away). Our results provide evidence that neuro-symbolic models are a promising direction for improving complex reasoning. We release QUITE and code for training and experiments on Github. © 2024 Association for Computational Linguistics.

关键词： Question answering

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When LLMs Meet Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large language Models for Depression Detection

When LLMs Meet Acoustic Landmarks: An Efficient Approach to ...

引用

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

作者： Zhang, Xiangyu Liu, Hexin Xu, Kaishuai Zhang, Qiquan Liu, Daijiao Ahmed, Beena Epps, Julien The University of New South Wales Australia Nanyang Technological University Singapore The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761643

Depression is a critical concern in global mental health, prompting extensive research into AI-based detection methods. Among various AI technologies, Large language Models (LLMs) stand out for their versatility in mental healthcare applications. However, their primary limitation arises from their exclusive dependence on textual input, which constrains their overall capabilities. Furthermore, the utilization of LLMs in identifying and analyzing depressive states is still relatively untapped. In this paper, we present an innovative approach to integrating acoustic speech information into the LLMs framework for multimodal depression detection. We investigate an efficient method for depression detection by integrating speech signals into LLMs utilizing Acoustic Landmarks. By incorporating acoustic landmarks, which are specific to the pronunciation of spoken words, our method adds critical dimensions to text transcripts. This integration also provides insights into the unique speech patterns of individuals, revealing the potential mental states of individuals. Evaluations of the proposed approach on the DAIC-WOZ dataset reveal state-of-the-art results when compared with existing Audio-Text baselines. In addition, this approach is not only valuable for the detection of depression but also represents a new perspective in enhancing the ability of LLMs to comprehend and process speech signals. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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DL-QAT: Weight-Decomposed Low-Rank Quantization-Aware Training for Large language Models

DL-QAT: Weight-Decomposed Low-Rank Quantization-Aware Traini...

引用

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

作者： Ke, Wenjin Li, Zhe Li, Dong Tian, Lu Barsoum, Emad Advanced Micro Devices Inc. Beijing China

ISBN: (纸本)9798891761667

Improving the efficiency of inference in Large language Models (LLMs) is a critical area of research. Post-training Quantization (PTQ) is a popular technique, but it often faces challenges at low-bit levels, particularly in downstream tasks. Quantization-aware Training (QAT) can alleviate this problem, but it requires significantly more computational resources. To tackle this, we introduced Weight-Decomposed Low-Rank Quantization-Aware Training (DL-QAT), which merges the advantages of QAT while training only less than 1% of the total parameters. Specifically, we introduce a group-specific quantization magnitude to adjust the overall scale of each quantization group. Within each quantization group, we use LoRA matrices to update the weight size and direction in the quantization space. We validated the effectiveness of our method on the LLaMA and LLaMA2 model families. The results show significant improvements over our baseline method across different quantization granularities. For instance, for LLaMA-7B, our approach outperforms the previous state-of-the-art method by 4.2% in MMLU on 3-bit LLaMA-7B model. Additionally, our quantization results on pre-trained models also surpass previous QAT methods, demonstrating the superior performance and efficiency of our approach. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Discourse Dependency Parsing with Sentence Dependency Parsing: A Unified Generative Method Based on Code Representation

Enhancing Discourse Dependency Parsing with Sentence Depende...

引用

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

作者： Shen, Zizhuo Shao, Yanqiu Li, Wei Beijing Language and Culture University China

ISBN: (纸本)9798891761681

Due to the high complexity of Discourse Dependency Parsing (DDP) tasks, their existing annotation resources are relatively scarce compared to other NLP tasks, and different DDP tasks also have significant differences in annotation schema. These issues have led to the dilemma of low resources for DDP tasks. Thanks to the powerful capabilities of Large language Models (LLMs) in cross-task learning, we can use LLMs to model dependency parsing under different annotation schema in an unified manner, in order to alleviate the dilemma of low resources for DDP tasks. However, enabling LLMs to deeply comprehend dependency parsing tasks is a challenge that remains underexplored. Inspired by the application of code-based methods in complex tasks, we propose a code-based unified dependency parsing method. We treat the process of dependency parsing as a search process of dependency paths and use code to represent this search process. Furthermore, we use a curriculum-learning based instruction tuning strategy for joint training of multiple dependency parsing tasks. The experimental results show that our proposed code-based DDP system has achieved good performance on two Chinese DDP tasks (especially significant improvement on the DDP task with relatively less training data). © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Is ChatGPT a Financial Expert? Evaluating language Models on Financial natural language processing

Is ChatGPT a Financial Expert? Evaluating Language Models on...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Guo, Yue Xu, Zian Yang, Yi Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798891760615

The emergence of Large language Models (LLMs), such as ChatGPT, has revolutionized general natural language preprocessing (NLP) tasks. However, their expertise in the financial domain lacks a comprehensive evaluation. To assess the ability of LLMs to solve financial NLP tasks, we present FinLMEval, a framework for Financial language Model Evaluation, comprising nine datasets designed to evaluate the performance of language models. This study compares the performance of encoder-only language models and the decoderonly language models. Our findings reveal that while some decoder-only LLMs demonstrate notable performance across most financial tasks via zero-shot prompting, they generally lag behind the fine-tuned expert models, especially when dealing with proprietary datasets. We hope this study provides foundation evaluations for continuing efforts to build more advanced LLMs in the financial domain.

关键词： Finance

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PERSONALIZED PIECES: Efficient Personalized Large language Models through Collaborative Efforts

PERSONALIZED PIECES: Efficient Personalized Large Language M...

引用

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

作者： Tan, Zhaoxuan Liu, Zheyuan Jiang, Meng University of Notre Dame United States

ISBN: (纸本)9798891761643

Personalized large language models (LLMs) aim to tailor interactions, content, and recommendations to individual user preferences. While parameter-efficient fine-tuning (PEFT) methods excel in performance and generalization, they are costly and limit communal benefits when used individually. To this end, we introduce PERSONALIZED PIECES (PER-PCS), a framework that allows users to safely share and assemble personalized PEFT efficiently with collaborative efforts. PER-PCS involves selecting sharers, breaking their PEFT into pieces, and training gates for each piece. These pieces are added to a pool, from which target users can select and assemble personalized PEFT using their history data. This approach preserves privacy and enables fine-grained user modeling without excessive storage and computation demands. Experimental results show PER-PCS outperforms non-personalized and PEFT retrieval baselines, offering performance comparable to OPPU with significantly lower resource use across six tasks. Further analysis highlights PER-PCS's robustness concerning sharer count and selection strategy, pieces sharing ratio, and scalability in computation time and storage space. PER-PCS's modularity promotes safe sharing, making LLM personalization more efficient, effective, and widely accessible through collaborative efforts. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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