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

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Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision language Models

Finer: Investigating and Enhancing Fine-Grained Visual Conce...

引用

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

作者： Kim, Jeonghwan Ji, Heng University of Illinois Urbana-Champaign United States

ISBN: (纸本)9798891761643

Recent advances in instruction-tuned Large Vision-language Models (LVLMs) have imbued the models with the ability to generate high-level, image-grounded explanations with ease. While such capability is largely attributed to the rich world knowledge contained within the Large language Models (LLMs), our work reveals their shortcomings in fine-grained visual categorization (FGVC) across six different benchmark settings. Most recent state-of-the-art LVLMs such as LLaVa-1.5, InstructBLIP and GPT-4V not only severely deteriorate in terms of classification performance, e.g., average drop of 65.58 in EM for Stanford Dogs for LLaVA-1.5, but also struggle to generate descriptive visual attributes based on a concept that appears within an input image despite their prominent zero-shot image captioning ability. In-depth analyses show that instruction-tuned LVLMs suffer from modality gap, showing discrepancy when given textual and visual inputs that correspond to the same concept. In an effort to further the community's endeavor in this direction, we propose a multiple granularity attribute-centric benchmark and training mixture, FINER, which aims to establish a ground to evaluate LVLMs' fine-grained visual comprehension ability and provide significantly improved explainability. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

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FROM WORDS TO WIRES: Generating Functioning Electronic Devices from natural language Descriptions

FROM WORDS TO WIRES: Generating Functioning Electronic Devic...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Jansen, Peter Univ Arizona Tucson AZ 85721 USA

ISBN: (纸本)9798891760615

In this work, we show that contemporary language models have a previously unknown skill - the capacity for electronic circuit design from high-level textual descriptions, akin to code generation. We introduce two benchmarks: PINS100, assessing model knowledge of electrical components, and MICRO25, evaluating a model's capability to design common micro-controller circuits and code in the ARDUINO ecosystem that involve input, output, sensors, motors, protocols, and logic - with models such as GPT-4 and Claude-V1 achieving between 60% to 96% PASS@ 1 on generating full devices. We include six case studies of using language models as a design assistant for moderately complex devices, such as a radiation-powered random number generator, an emoji keyboard, a visible spectrometer, and several assistive devices, while offering a qualitative analysis performance, outlining evaluation challenges, and suggesting areas of development to improve complex circuit design and practical utility. With this work, we aim to spur research at the juncture of natural language processing and electronic design.

关键词： Random number generation

来源：评论

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A Comprehensive Survey of Scientific Large language Models and Their Applications in Scientific Discovery

A Comprehensive Survey of Scientific Large Language Models a...

引用

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

作者： Zhang, Yu Chen, Xiusi Jin, Bowen Wang, Sheng Ji, Shuiwang Wang, Wei Han, Jiawei University of Illinois Urbana-Champaign United States University of California Los Angeles United States University of Washington Seattle United States Texas A&M University United States

ISBN: (纸本)9798891761643

In many scientific fields, large language models (LLMs) have revolutionized the way text and other modalities of data (e.g., molecules and proteins) are handled, achieving superior performance in various applications and augmenting the scientific discovery process. Nevertheless, previous surveys on scientific LLMs often concentrate on one or two fields or a single modality. In this paper, we aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs regarding their architectures and pretraining techniques. To this end, we comprehensively survey over 260 scientific LLMs, discuss their commonalities and differences, as well as summarize pre-training datasets and evaluation tasks for each field and modality. Moreover, we investigate how LLMs have been deployed to benefit scientific discovery. Resources related to this survey are available at https://***/yuzhimanhua/Awesome-Scientific-language-Models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding

MIND: Multimodal Shopping Intention Distillation from Large ...

引用

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

作者： Xu, Baixuan Wang, Weiqi Shi, Haochen Ding, Wenxuan Jing, Huihao Fang, Tianqing Bai, Jiaxin Liu, Xin Yu, Changlong Li, Zheng Luo, Chen Yin, Qingyu Yin, Bing Chen, Long Song, Yangqiu Department of Computer Science and Engineering HKUST Hong Kong *** Inc. Palo AltoCA United States

ISBN: (纸本)9798891761643

Improving user experience and providing personalized search results in E-commerce services heavily rely on understanding purchase intention. However, existing methods for acquiring large-scale intentions bank on distilling large language models with human annotation for verification. Such an approach tends to generate product-centric intentions, overlook valuable visual information from product images, and incurs high costs for scalability. To address these issues, we introduce MIND, a multimodal framework that allows Large Vision-language Models (LVLMs) to infer purchase intentions from multimodal product metadata and prioritize human-centric ones. Using Amazon Review data, we apply MIND and create a multimodal intention knowledge base, which contains 1,264,441 intentions derived from 126,142 co-buy shopping records across 107,215 products. Extensive human evaluations demonstrate the high plausibility and typicality of our obtained intentions and validate the effectiveness of our distillation framework and filtering mechanism. Further experiments reveal the positive downstream benefits that MIND brings to intention comprehension tasks and highlight the importance of multimodal generation and role-aware filtering. Additionally, MIND shows robustness to different prompts and superior generation quality compared to previous methods. Our code and data are publicly available at https://***/HKUSTKnowComp/MIND_Distillation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Injecting structural hints: Using language models to study inductive biases in language learning

Injecting structural hints: Using language models to study i...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Papadimitriou, Isabel Jurafsky, Dan Stanford Univ Comp Sci Dept Stanford CA 94305 USA

ISBN: (纸本)9798891760615

Both humans and large language models are able to learn language without explicit structural supervision. What inductive biases make this learning possible? We address this fundamental cognitive question by leveraging transformer language models: we inject inductive bias into language models by pretraining on formally-structured data, and then evaluate the biased learners' ability to learn typologicallydiverse natural languages. Our experimental setup creates a testbed for hypotheses about inductive bias in human language learning. We investigate the effect of injecting models with three types of inductive bias: 1) recursive, hierarchical processing, 2) crossing token-token relationships that can't be modeled by contextfree grammars, and 3) a Zipfian power-law vocabulary distribution. We show that noncontext-free relationships form the best inductive biases. Our study leverages the capabilities of transformer models to run controlled language learning experiments that are not possible to run on humans, and surfaces hypotheses about the structures that facilitate language learning in both humans and machines.

关键词： Context free grammars

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Knowledge-Augmented language Model Verification

Knowledge-Augmented Language Model Verification

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Baek, Jinheon Jeong, Soyeong Kang, Minki Park, Jong C. Hwang, Sung Ju Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798891760608

Recent language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters. Yet, LMs often generate the factually incorrect responses to the given queries, since their knowledge may be inaccurate, incomplete, and outdated. To address this problem, previous works propose to augment LMs with the knowledge retrieved from an external knowledge source. However, such approaches often show suboptimal text generation performance due to two reasons: 1) the model may fail to retrieve the knowledge relevant to the given query, or 2) the model may not faithfully reflect the retrieved knowledge in the generated text. To overcome these, we propose to verify the output and the knowledge of the knowledge-augmented LMs with a separate verifier, which is a small LM that is trained to detect those two types of errors through instruction-finetuning. Then, when the verifier recognizes an error, we can rectify it by either retrieving new knowledge or generating new text. Further, we use an ensemble of the outputs from different instructions with a single verifier to enhance the reliability of the verification processes. We validate the effectiveness of the proposed verification steps on multiple question answering benchmarks, whose results show that the proposed verifier effectively identifies retrieval and generation errors, allowing LMs to provide more factually correct outputs. Our code is available at https://***/JinheonBaek/KALMV.

关键词： Errors

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Construction and Application of Text Classification Model under natural language processing 24

Construction and Application of Text Classification Model un...

引用

International conference on Modeling, natural language processing and Machine Learning (CMNM)

作者： Sun, Zhongnuo Gao, Pan Technol Vocat Coll Dezhou Dezhou Lectromech Engn Sch Yucheng 251200 Shandong Peoples R China

ISBN: (纸本)9798400709760

With the prevalence of the Internet and various types of social media, our daily life is surrounded by a huge amount of text information, which can provide us with the convenience of accessing information and communication, but also brings challenges in management and maintenance. In the face of the current practical needs, traditional machine learning methods, such as TF-IDF, SVM, Naive Bayes need to rely on manual completion of the feature design, and can not solve the problem of data sparsity and high-dimensional feature vectors, so that the accuracy of text classification is insufficient. In this regard, based on the deep learning algorithm, this paper will propose a set of Chinese text classification model based on Word2vec and TextCNN to realize the automatic classification function of a large number of texts. Practice has proved that the test precision of this classification model is 94.12%, the recall is 92.19%, and the F1 value is 93.11%. Compared with the traditional TF-IDF, SVM and Naive Bayes methods, all the evaluation indexes are obviously improved, and the actual application effect is better than that of the original TextCNN. The overall design is in line with expectations and has certain application and promotion value.

关键词： natural language processing Text classification Convolutional neural network Word2vec TextCNN

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Query-OPT: Optimizing Inference of Large language Models via Multi-Query Instructions in Meeting Summarization

Query-OPT: Optimizing Inference of Large Language Models via...

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

作者： Laskar, Md Tahmid Rahman Khasanova, Elena Fu, Xue-Yong Chen, Cheng Bhushan, Shashi T.N. Dialpad Inc VancouverBC Canada

ISBN: (纸本)9798891761667

This work focuses on the task of query-based meeting summarization, in which the summary of a context (meeting transcript) is generated in response to a specific query. When using Large language Models (LLMs) for this task, usually a new call to the LLM inference endpoint/API is triggered for each new query, even if the context stays the same. However, repeated calls to the LLM inference endpoints would significantly increase the costs of using them in production, making LLMs impractical for many real-world use cases. To address this problem, in this paper, we investigate whether combining the queries for the same input context in a single prompt to minimize repeated calls can be successfully used in meeting summarization. In this regard, we conduct extensive experiments by comparing the performance of various popular LLMs: GPT-4, Gemini-1.5, Claude-3, LLaMA-2, Mistral, Phi-3, and Qwen-2 in single-query and multi-query settings. We observe that 100% reliability in generating the response in the expected format is usually limited to certain closed-source LLMs, with most open-source LLMs lagging behind (except a few 7B parameters LLMs like Mistral and Phi-3). We conclude that multi-query prompting could be useful to significantly optimize the inference costs in meeting summarization. © 2024 Association for Computational Linguistics.

关键词： Structured Query language

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Fisher Information-based Efficient Curriculum Federated Learning with Large language Models

Fisher Information-based Efficient Curriculum Federated Lear...

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

作者： Liu, Ji Ren, Jiaxiang Jin, Ruoming Zhang, Zijie Zhou, Yang Valduriez, Patrick Dou, Dejing HiThink Research Zhejiang Hangzhou China Auburn University Auburn United States Kent State University Kent United States University of Texas at San Antonio San Antonio United States Inria University of Montpellier CNRS LIRMM France LNCC Petropolis Brazil Fudan University Shanghai China BEDI Cloud Beijing China

ISBN: (纸本)9798891761643

As a promising paradigm to collaboratively train models with decentralized data, Federated Learning (FL) can be exploited to fine-tune Large language Models (LLMs). While LLMs correspond to huge size, the scale of the training data significantly increases, which leads to tremendous amounts of computation and communication costs. The training data is generally non-Independent and Identically Distributed (non-IID), which requires adaptive data processing within each device. Although Low-Rank Adaptation (LoRA) can significantly reduce the scale of parameters to update in the fine-tuning process, it still takes unaffordable time to transfer the low-rank parameters of all the layers in LLMs. In this paper, we propose a Fisher Information-based Efficient Curriculum Federated Learning framework (FibecFed) with two novel methods, i.e., adaptive federated curriculum learning and efficient sparse parameter update. First, we propose a fisher information-based method to adaptively sample data within each device to improve the effectiveness of the FL fine-tuning process. Second, we dynamically select the proper layers for global aggregation and sparse parameters for local update with LoRA so as to improve the efficiency of the FL fine-tuning process. Extensive experimental results based on 10 datasets demonstrate that FibecFed yields excellent performance (up to 45.35% in terms of accuracy) and superb fine-tuning speed (up to 98.61% faster) compared with 17 baseline approaches). Our code will be publicly available. © 2024 Association for Computational Linguistics.

关键词： Federated learning

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Are Large language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions

Are Large Language Models Good Classifiers? A Study on Edit ...

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

作者： Ruan, Qian Kuznetsov, Ilia Gurevych, Iryna Technical University of Darmstadt Germany

ISBN: (纸本)9798891761643

Classification is a core NLP task architecture with many potential applications. While large language models (LLMs) have brought substantial advancements in text generation, their potential for enhancing classification tasks remains underexplored. To address this gap, we propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task. Our extensive experiments and systematic comparisons with various training approaches and a representative selection of LLMs yield new insights into their application for EIC. We investigate the generalizability of these findings on five further classification tasks. To demonstrate the proposed methods and address the data shortage for empirical edit analysis, we use our best-performing EIC model to create Re3-Sci2.0, a new large-scale dataset of 1,780 scientific document revisions with over 94k labeled edits. The quality of the dataset is assessed through human evaluation. The new dataset enables an in-depth empirical study of human editing behavior in academic writing. We make our experimental framework, models and data2 publicly available. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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