Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property Prediction

• URL: http://arxiv.org/abs/2405.10448v2
• Date: Fri, 2 Aug 2024 20:07:30 GMT
• Title: Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property Prediction
• Authors: Chinedu Ekuma,
• Abstract summary: PropertyExtractor is an open-source tool that blends zero-shot with few-shot in-context learning. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of approximately 9%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs like Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies - enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95\% with an error rate of approximately 9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. Specifically for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.

Related papers

• Large Language Models and Synthetic Data for Monitoring Dataset Mentions in Research Papers [0.0]
  This paper presents a machine learning framework that automates dataset mention detection across research domains. We employ zero-shot extraction from research papers, an LLM-as-a-Judge for quality assessment, and a reasoning agent for refinement to generate a weakly supervised synthetic dataset. At inference, a ModernBERT-based classifier efficiently filters dataset mentions, reducing computational overhead while maintaining high recall.
  arXiv  Detail & Related papers  (2025-02-14T16:16:02Z)
• MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale [66.73529246309033]
  multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. Existing instruction-tuning datasets only provide phrase-level answers without any intermediate rationales. We introduce a scalable and cost-effective method to construct a large-scale multimodal instruction-tuning dataset with rich intermediate rationales.
  arXiv  Detail & Related papers  (2024-12-06T18:14:24Z)
• Evaluating Language Models as Synthetic Data Generators [74.80905172696366]
  AgoraBench is a benchmark that provides standardized settings and metrics to evaluate LMs' data generation abilities. Through synthesizing 1.26 million training instances using 6 LMs and training 99 student models, we uncover key insights about LMs' data generation capabilities.
  arXiv  Detail & Related papers  (2024-12-04T19:20:32Z)
• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials [34.8008617873679]
  We find that multi-task neural networks can learn from multi-modal data and outperform single-task models trained for specific properties. As expected, the improvement is more significant for data-scarce properties. This approach is widely applicable to fields outside energetic materials.
  arXiv  Detail & Related papers  (2024-08-21T12:54:26Z)
• Enhancing Knowledge Retrieval with In-Context Learning and Semantic Search through Generative AI [3.9773527114058855]
  We propose a novel methodology that combines the generative capabilities of Large Language Models with the fast and accurate retrieval capabilities of vector databases. The developed model, Generative Text Retrieval (GTR), is adaptable to both unstructured and structured data with minor refinement. The refined model, Generative Tabular Text Retrieval (GTR-T), demonstrated its efficiency in large database querying.
  arXiv  Detail & Related papers  (2024-06-13T23:08:06Z)
• 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)
• LESS: Selecting Influential Data for Targeted Instruction Tuning [64.78894228923619]
  We propose LESS, an efficient algorithm to estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. We show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Our method goes beyond surface form cues to identify data that the necessary reasoning skills for the intended downstream application.
  arXiv  Detail & Related papers  (2024-02-06T19:18:04Z)
• Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT [9.33544942080883]
  This article presents a new natural language processing (NLP) task called structured information inference (SII) to address the complexities of information extraction at the device level in materials science. We accomplished this task by tuning GPT-3 on an existing perovskite solar cell FAIR dataset with 91.8% F1-score and extended the dataset with data published since its release. We also designed experiments to predict the electrical performance of solar cells and design materials or devices with targeted parameters using large language models (LLMs)
  arXiv  Detail & Related papers  (2023-04-05T04:01:52Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery [1.0036312061637764]
  Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure-property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is scarcely populated and of dubious quality. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure-property relationships from the literature.
  arXiv  Detail & Related papers  (2021-11-02T21:43:58Z)

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.