Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesis

• URL: http://arxiv.org/abs/2411.10878v1
• Date: Sat, 16 Nov 2024 20:18:57 GMT
• Title: Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesis
• Authors: Jawad Ibn Ahad, Rafeed Mohammad Sultan, Abraham Kaikobad, Fuad Rahman, Mohammad Ruhul Amin, Nabeel Mohammed, Shafin Rahman,
• Abstract summary: This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs) Our research introduces a novel approach that fine-tunes the LLM on extensive scientific datasets to address challenges in big data handling and structured data extraction.
• Score: 7.059964549363294
• License:
• Abstract: This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes the findings of multiple studies support articles to provide a comprehensive understanding. We know that a meta-article provides a structured analysis of several articles. However, conducting meta-analysis by hand is labor-intensive, time-consuming, and susceptible to human error, highlighting the need for automated pipelines to streamline the process. Our research introduces a novel approach that fine-tunes the LLM on extensive scientific datasets to address challenges in big data handling and structured data extraction. We automate and optimize the meta-analysis process by integrating Retrieval Augmented Generation (RAG). Tailored through prompt engineering and a new loss metric, Inverse Cosine Distance (ICD), designed for fine-tuning on large contextual datasets, LLMs efficiently generate structured meta-analysis content. Human evaluation then assesses relevance and provides information on model performance in key metrics. This research demonstrates that fine-tuned models outperform non-fine-tuned models, with fine-tuned LLMs generating 87.6% relevant meta-analysis abstracts. The relevance of the context, based on human evaluation, shows a reduction in irrelevancy from 4.56% to 1.9%. These experiments were conducted in a low-resource environment, highlighting the study's contribution to enhancing the efficiency and reliability of meta-analysis automation.

Related papers

• Deciphering genomic codes using advanced NLP techniques: a scoping review [13.188820316979806]
  The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of Natural Language Processing (NLP) techniques, particularly Large Language Models (LLMs) and transformer architectures.
  arXiv  Detail & Related papers  (2024-11-25T04:35:56Z)
• Utilizing Large Language Models for Event Deconstruction to Enhance Multimodal Aspect-Based Sentiment Analysis [2.1329326061804816]
  This paper introduces Large Language Models (LLMs) for event decomposition and proposes a reinforcement learning framework for Multimodal Aspect-based Sentiment Analysis (MABSA-RL) Experimental results show that MABSA-RL outperforms existing advanced methods on two benchmark datasets.
  arXiv  Detail & Related papers  (2024-10-18T03:40:45Z)
• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models [19.72316842477808]
  We evaluate whether modern large language models (LLMs) can reliably perform this task. Massive LLMs that can accommodate lengthy inputs are tantalizingly close to realizing fully automatic meta-analysis.
  arXiv  Detail & Related papers  (2024-05-02T19:20:11Z)
• DACO: Towards Application-Driven and Comprehensive Data Analysis via Code Generation [83.30006900263744]
  Data analysis is a crucial analytical process to generate in-depth studies and conclusive insights. We propose to automatically generate high-quality answer annotations leveraging the code-generation capabilities of LLMs. Our DACO-RL algorithm is evaluated by human annotators to produce more helpful answers than SFT model in 57.72% cases.
  arXiv  Detail & Related papers  (2024-03-04T22:47:58Z)
• Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis [6.592797748561459]
  Large Language Models (LLMs) have enabled collaborative human-bot interactions in Software Engineering (SE) We introduce a new dimension of scalability and accuracy in qualitative research, potentially transforming data interpretation methodologies in SE.
  arXiv  Detail & Related papers  (2024-02-02T13:10:46Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data [66.11139091362078]
  We provide the first model selection results on large pretrained Transformers from Huggingface using generalization metrics. Despite their niche status, we find that metrics derived from the heavy-tail (HT) perspective are particularly useful in NLP tasks.
  arXiv  Detail & Related papers  (2022-02-06T20:07:35Z)
• Automated Meta-Analysis: A Causal Learning Perspective [30.746257517698133]
  We work toward automating meta-analysis with a focus on controlling for risks of bias. We first extract information from scientific publications written in natural language. From a novel causal learning perspective, we propose to frame automated meta-analysis as a multiple-causal-inference problem.
  arXiv  Detail & Related papers  (2021-04-09T23:07:07Z)
• Meta-learning framework with applications to zero-shot time-series forecasting [82.61728230984099]
  This work provides positive evidence using a broad meta-learning framework. residual connections act as a meta-learning adaptation mechanism. We show that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining.
  arXiv  Detail & Related papers  (2020-02-07T16:39:43Z)

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.