Efficacy of Large Language Models in Systematic Reviews

• URL: http://arxiv.org/abs/2408.04646v2
• Date: Sat, 26 Oct 2024 20:01:55 GMT
• Title: Efficacy of Large Language Models in Systematic Reviews
• Authors: Aaditya Shah, Shridhar Mehendale, Siddha Kanthi,
• Abstract summary: This study investigates the effectiveness of Large Language Models (LLMs) in interpreting existing literature. We compiled and hand-coded a database of 88 relevant papers published from March 2020 to May 2024. We evaluated two current state-of-the-art LLMs, Meta AI's Llama 3 8B and OpenAI's GPT-4o, on the accuracy of their interpretations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study investigates the effectiveness of Large Language Models (LLMs) in interpreting existing literature through a systematic review of the relationship between Environmental, Social, and Governance (ESG) factors and financial performance. The primary objective is to assess how LLMs can replicate a systematic review on a corpus of ESG-focused papers. We compiled and hand-coded a database of 88 relevant papers published from March 2020 to May 2024. Additionally, we used a set of 238 papers from a previous systematic review of ESG literature from January 2015 to February 2020. We evaluated two current state-of-the-art LLMs, Meta AI's Llama 3 8B and OpenAI's GPT-4o, on the accuracy of their interpretations relative to human-made classifications on both sets of papers. We then compared these results to a "Custom GPT" and a fine-tuned GPT-4o Mini model using the corpus of 238 papers as training data. The fine-tuned GPT-4o Mini model outperformed the base LLMs by 28.3% on average in overall accuracy on prompt 1. At the same time, the "Custom GPT" showed a 3.0% and 15.7% improvement on average in overall accuracy on prompts 2 and 3, respectively. Our findings reveal promising results for investors and agencies to leverage LLMs to summarize complex evidence related to ESG investing, thereby enabling quicker decision-making and a more efficient market.

Related papers

• LLM2: Let Large Language Models Harness System 2 Reasoning [65.89293674479907]
  Large language models (LLMs) have exhibited impressive capabilities across a myriad of tasks, yet they occasionally yield undesirable outputs. We introduce LLM2, a novel framework that combines an LLM with a process-based verifier. LLMs2 is responsible for generating plausible candidates, while the verifier provides timely process-based feedback to distinguish desirable and undesirable outputs.
  arXiv  Detail & Related papers  (2024-12-29T06:32:36Z)
• AI Predicts AGI: Leveraging AGI Forecasting and Peer Review to Explore LLMs' Complex Reasoning Capabilities [0.3745329282477066]
  We tasked 16 state-of-the-art large language models with estimating the likelihood of Artificial General Intelligence (AGI) emerging by 2030. To assess the quality of these forecasts, we implemented an automated peer review process (LLM-PR)
  arXiv  Detail & Related papers  (2024-12-12T15:52:41Z)
• A Large-Scale Study of Relevance Assessments with Large Language Models: An Initial Look [52.114284476700874]
  This paper reports on the results of a large-scale evaluation (the TREC 2024 RAG Track) where four different relevance assessment approaches were deployed. We find that automatically generated UMBRELA judgments can replace fully manual judgments to accurately capture run-level effectiveness. Surprisingly, we find that LLM assistance does not appear to increase correlation with fully manual assessments, suggesting that costs associated with human-in-the-loop processes do not bring obvious tangible benefits.
  arXiv  Detail & Related papers  (2024-11-13T01:12:35Z)
• The emergence of Large Language Models (LLM) as a tool in literature reviews: an LLM automated systematic review [42.112100361891905]
  This study aims to summarize the usage of Large Language Models (LLMs) in the process of creating a scientific review. We look at the range of stages in a review that can be automated and assess the current state-of-the-art research projects in the field.
  arXiv  Detail & Related papers  (2024-09-06T20:12:57Z)
• Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning [17.83428132220955]
  We propose a pre-retrieval framework named Pseudo-Graph Retrieval-Augmented Generation (PG-RAG) PG-RAG conceptualizes LLMs as students by providing them with abundant raw reading materials. During the retrieval phase, PG-RAG mimics the human behavior in flipping through notes.
  arXiv  Detail & Related papers  (2024-05-27T08:26:45Z)
• Exploring the use of a Large Language Model for data extraction in systematic reviews: a rapid feasibility study [0.28318468414401093]
  This paper describes a rapid feasibility study of using GPT-4, a large language model (LLM), to (semi)automate data extraction in systematic reviews. Overall, results indicated an accuracy of around 80%, with some variability between domains.
  arXiv  Detail & Related papers  (2024-05-23T11:24:23Z)
• Split and Merge: Aligning Position Biases in LLM-based Evaluators [22.265542509143756]
  PORTIA is an alignment-based system designed to mimic human comparison strategies to calibrate position bias. Our results show that PORTIA markedly enhances the consistency rates for all the models and comparison forms tested. It rectifies around 80% of the position bias instances within the GPT-4 model, elevating its consistency rate up to 98%.
  arXiv  Detail & Related papers  (2023-09-29T14:38:58Z)
• L-Eval: Instituting Standardized Evaluation for Long Context Language Models [91.05820785008527]
  We propose L-Eval to institute a more standardized evaluation for long context language models (LCLMs) We build a new evaluation suite containing 20 sub-tasks, 508 long documents, and over 2,000 human-labeled query-response pairs. Results show that popular n-gram matching metrics generally can not correlate well with human judgment.
  arXiv  Detail & Related papers  (2023-07-20T17:59:41Z)
• Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting [65.00288634420812]
  Pairwise Ranking Prompting (PRP) is a technique to significantly reduce the burden on Large Language Models (LLMs) Our results are the first in the literature to achieve state-of-the-art ranking performance on standard benchmarks using moderate-sized open-sourced LLMs.
  arXiv  Detail & Related papers  (2023-06-30T11:32:25Z)
• Prompting GPT-3 To Be Reliable [117.23966502293796]
  This work decomposes reliability into four facets: generalizability, fairness, calibration, and factuality. We find that GPT-3 outperforms smaller-scale supervised models by large margins on all these facets.
  arXiv  Detail & Related papers  (2022-10-17T14:52:39Z)
• A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges [73.34944216896837]
  Legal judgment prediction (LJP) applies Natural Language Processing (NLP) techniques to predict judgment results based on fact descriptions automatically. We analyze 31 LJP datasets in 6 languages, present their construction process and define a classification method of LJP. We show the state-of-art results for 8 representative datasets from different court cases and discuss the open challenges.
  arXiv  Detail & Related papers  (2022-04-11T04:06:28Z)

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.