Benchmarking Large Language Models with Augmented Instructions for Fine-grained Information Extraction

• URL: http://arxiv.org/abs/2310.05092v1
• Date: Sun, 8 Oct 2023 09:41:18 GMT
• Title: Benchmarking Large Language Models with Augmented Instructions for Fine-grained Information Extraction
• Authors: Jun Gao, Huan Zhao, Yice Zhang, Wei Wang, Changlong Yu, Ruifeng Xu
• Abstract summary: This paper introduces a fine-grained IE benchmark dataset tailored for Large Language Models (LLMs) Through extensive evaluations, we observe that encoder-decoder models, particularly T5 and FLAN-T5, perform well in generalizing to unseen information types.
• Score: 46.09887436555637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Information Extraction (IE) is an essential task in Natural Language Processing. Traditional methods have relied on coarse-grained extraction with simple instructions. However, with the emergence of Large Language Models (LLMs), there is a need to adapt IE techniques to leverage the capabilities of these models. This paper introduces a fine-grained IE benchmark dataset tailored for LLMs, employing augmented instructions for each information type, which includes task descriptions, extraction rules, output formats, and examples. Through extensive evaluations, we observe that encoder-decoder models, particularly T5 and FLAN-T5, perform well in generalizing to unseen information types, while ChatGPT exhibits greater adaptability to new task forms. Our results also indicate that performance is not solely dictated by model scale, and highlight the significance of architecture, data diversity, and learning techniques. This work paves the way for a more refined and versatile utilization of LLMs in Information Extraction.

Related papers

• Exploring Large Language Models for Product Attribute Value Identification [25.890927969633196]
  Product attribute value identification (PAVI) involves automatically identifying attributes and their values from product information. Existing methods rely on fine-tuning pre-trained language models, such as BART and T5. This paper explores large language models (LLMs), such as LLaMA and Mistral, as data-efficient and robust alternatives for PAVI.
  arXiv  Detail & Related papers  (2024-09-19T12:09:33Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks [0.0]
  We will introduce a new kind of GLiNER model that can be used for various information extraction tasks while being a small encoder model. Our model achieved SoTA performance on zero-shot NER benchmarks and leading performance on question-answering, summarization and relation extraction tasks.
  arXiv  Detail & Related papers  (2024-06-14T13:54:29Z)
• Leveraging Large Language Models for Web Scraping [0.0]
  This research investigates a general-purpose accurate data scraping recipe for RAG models designed for language generation. To capture knowledge in a more modular and interpretable way, we use pre trained language models with a latent knowledge retriever.
  arXiv  Detail & Related papers  (2024-06-12T14:15:15Z)
• Large Language Models for Data Annotation: A Survey [49.8318827245266]
  The emergence of advanced Large Language Models (LLMs) presents an unprecedented opportunity to automate the complicated process of data annotation. This survey includes an in-depth taxonomy of data types that LLMs can annotate, a review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation.
  arXiv  Detail & Related papers  (2024-02-21T00:44:04Z)
• Learning to Extract Structured Entities Using Language Models [52.281701191329]
  Recent advances in machine learning have significantly impacted the field of information extraction. We reformulate the task to be entity-centric, enabling the use of diverse metrics. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP metric.
  arXiv  Detail & Related papers  (2024-02-06T22:15:09Z)
• GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect [0.0]
  We introduce the General Information Extraction Large Language Model (GIELLM) It integrates text Classification, Sentiment Analysis, Named Entity Recognition, Relation Extraction, and Event Extraction using a uniform input-output schema. This innovation marks the first instance of a model simultaneously handling such a diverse array of IE subtasks.
  arXiv  Detail & Related papers  (2023-11-12T13:30:38Z)
• Instruct and Extract: Instruction Tuning for On-Demand Information Extraction [86.29491354355356]
  On-Demand Information Extraction aims to fulfill the personalized demands of real-world users. We present a benchmark named InstructIE, inclusive of both automatically generated training data, as well as the human-annotated test set. Building on InstructIE, we further develop an On-Demand Information Extractor, ODIE.
  arXiv  Detail & Related papers  (2023-10-24T17:54:25Z)
• Language models are weak learners [71.33837923104808]
  We show that prompt-based large language models can operate effectively as weak learners. We incorporate these models into a boosting approach, which can leverage the knowledge within the model to outperform traditional tree-based boosting. Results illustrate the potential for prompt-based LLMs to function not just as few-shot learners themselves, but as components of larger machine learning pipelines.
  arXiv  Detail & Related papers  (2023-06-25T02:39:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.