A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models

• URL: http://arxiv.org/abs/2401.00544v2
• Date: Tue, 2 Jan 2024 03:03:18 GMT
• Title: A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models
• Authors: Vansh Sharma and Venkat Raman
• Abstract summary: The study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. The framework consistently delivers accurate domain-specific responses with minimal human oversight.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The multifaceted nature of combustion research emphasizes the critical role of knowledge processing in navigating and extracting valuable information from a vast and diverse pool of sources. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. It incorporates prompt engineering and offline open-source LLMs, offering user autonomy in selecting base models. The study provides a thorough examination of text segmentation strategies, conducts comparative studies between LLMs, and explores various optimized prompts to demonstrate the effectiveness of the framework. By incorporating an external database, the framework outperforms a conventional LLM in generating accurate responses and constructing robust arguments. Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature. The research addresses concerns related to hallucinations and false research articles by introducing a custom workflow developed with a detection algorithm to filter out inaccuracies. Despite identified areas for improvement, the framework consistently delivers accurate domain-specific responses with minimal human oversight. The prompt-agnostic approach introduced holds promise for future deliberations. The study underscores the significance of integrating LLMs and knowledge processing techniques in scientific research, providing a foundation for advancements in data assimilation and utilization.

Related papers

• Technical Report: Enhancing LLM Reasoning with Reward-guided Tree Search [95.06503095273395]
  o1-like reasoning approach is challenging, and researchers have been making various attempts to advance this open area of research. We present a preliminary exploration into enhancing the reasoning abilities of LLMs through reward-guided tree search algorithms.
  arXiv  Detail & Related papers  (2024-11-18T16:15:17Z)
• Experiences from Using LLMs for Repository Mining Studies in Empirical Software Engineering [12.504438766461027]
  Large Language Models (LLMs) have transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories. Our research packages a framework, coined Prompt Refinement and Insights for Mining Empirical Software repositories (PRIMES) Our findings indicate that standardizing prompt engineering and using PRIMES can enhance the reliability and accuracy of studies utilizing LLMs.
  arXiv  Detail & Related papers  (2024-11-15T06:08:57Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• A Quick, trustworthy spectral knowledge Q&A system leveraging retrieval-augmented generation on LLM [0.0]
  Large Language Model (LLM) has demonstrated significant success in a range of natural language processing (NLP) tasks within general domain. We introduce the Spectral Detection and Analysis Based Paper (SDAAP) dataset, which is the first open-source textual knowledge dataset for spectral analysis and detection. We also designed an automated Q&A framework based on the SDAAP dataset, which can retrieve relevant knowledge and generate high-quality responses.
  arXiv  Detail & Related papers  (2024-08-21T12:09:37Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning [0.9110413356918055]
  This research pioneers the use of fine-tuned Large Language Models (LLMs) to automate Systematic Literature Reviews ( SLRs) Our study employed the latest fine-tuning methodologies together with open-sourced LLMs, and demonstrated a practical and efficient approach to automating the final execution stages of an SLR process. The results maintained high fidelity in factual accuracy in LLM responses, and were validated through the replication of an existing PRISMA-conforming SLR.
  arXiv  Detail & Related papers  (2024-04-08T00:08:29Z)
• The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark [6.116258355914058]
  This study conduct an extensive Brain-computer interfaces (BCI) analysis on open electroencephalography datasets. The need for such benchmark lies in the rapid industrial progress that has given rise to undisclosed proprietary solutions.
  arXiv  Detail & Related papers  (2024-04-03T15:18:50Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture [0.0]
  This study presents a method for implementing generative AI services by utilizing the Large Language Models (LLM) application architecture. The research delves into strategies for mitigating the issue of inadequate data, offering tailored solutions. A significant contribution of this work is the development of a Retrieval-Augmented Generation (RAG) model.
  arXiv  Detail & Related papers  (2023-09-03T07:03:17Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)

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.