Comparing Open-Source and Commercial LLMs for Domain-Specific Analysis and Reporting: Software Engineering Challenges and Design Trade-offs

• URL: http://arxiv.org/abs/2509.24344v1
• Date: Mon, 29 Sep 2025 06:46:37 GMT
• Title: Comparing Open-Source and Commercial LLMs for Domain-Specific Analysis and Reporting: Software Engineering Challenges and Design Trade-offs
• Authors: Theo Koraag, Niklas Wagner, Felix Dobslaw, Lucas Gren,
• Abstract summary: Large Language Models (LLMs) enable automation of complex natural language processing across domains.<n>This study explored open-source and commercial LLMs for financial report analysis and commentary generation.
• Score: 3.5057035107656733
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Context: Large Language Models (LLMs) enable automation of complex natural language processing across domains, but research on domain-specific applications like Finance remains limited. Objectives: This study explored open-source and commercial LLMs for financial report analysis and commentary generation, focusing on software engineering challenges in implementation. Methods: Using Design Science Research methodology, an exploratory case study iteratively designed and evaluated two LLM-based systems: one with local open-source models in a multi-agent workflow, another using commercial GPT-4o. Both were assessed through expert evaluation of real-world financial reporting use cases. Results: LLMs demonstrated strong potential for automating financial reporting tasks, but integration presented significant challenges. Iterative development revealed issues including prompt design, contextual dependency, and implementation trade-offs. Cloud-based models offered superior fluency and usability but raised data privacy and external dependency concerns. Local open-source models provided better data control and compliance but required substantially more engineering effort for reliability and usability. Conclusion: LLMs show strong potential for financial reporting automation, but successful integration requires careful attention to architecture, prompt design, and system reliability. Implementation success depends on addressing domain-specific challenges through tailored validation mechanisms and engineering strategies that balance accuracy, control, and compliance.

Related papers

• FinSight: Towards Real-World Financial Deep Research [68.31086471310773]
  FinSight is a novel framework for producing high-quality, multimodal financial reports.<n>To ensure professional-grade visualization, we propose an Iterative Vision-Enhanced Mechanism.<n>A two-stage Writing Framework expands concise Chain-of-Analysis segments into coherent, citation-aware, and multimodal reports.
  arXiv  Detail & Related papers  (2025-10-19T14:05:35Z)
• EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements [7.259647868714988]
  We introduce EDINET-Bench, an open-source Japanese financial benchmark to evaluate the performance of large language models (LLMs)<n>Our experiments reveal that even state-of-the-art LLMs struggle, performing only slightly better than logistic regression in binary classification for fraud detection and earnings forecasting.<n>Our dataset, benchmark construction code, and evaluation code is publicly available to facilitate future research in finance with LLMs.
  arXiv  Detail & Related papers  (2025-06-10T13:03:36Z)
• QuantMCP: Grounding Large Language Models in Verifiable Financial Reality [0.43512163406552007]
  Large Language Models (LLMs) hold immense promise for revolutionizing financial analysis and decision-making.<n>However, their direct application is often hampered by issues of data hallucination and lack of access to real-time, verifiable financial information.<n>This paper introduces QuantMCP, a novel framework designed to rigorously ground LLMs in financial reality.
  arXiv  Detail & Related papers  (2025-06-07T01:52:39Z)
• ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges [72.19809898215857]
  We introduce ModelingBench, a novel benchmark featuring real-world-inspired, open-ended problems from math modeling competitions across diverse domains.<n>These tasks require translating natural language into formal mathematical formulations, applying appropriate tools, and producing structured, defensible reports.<n>We also present ModelingAgent, a multi-agent framework that coordinates tool use, supports structured, creative solutions, and generates well-grounded, creative solutions.
  arXiv  Detail & Related papers  (2025-05-21T03:33:23Z)
• Evaluating Large Language Models for Real-World Engineering Tasks [75.97299249823972]
  This paper introduces a curated database comprising over 100 questions derived from authentic, production-oriented engineering scenarios.<n>Using this dataset, we evaluate four state-of-the-art Large Language Models (LLMs)<n>Our results show that LLMs demonstrate strengths in basic temporal and structural reasoning but struggle significantly with abstract reasoning, formal modeling, and context-sensitive engineering logic.
  arXiv  Detail & Related papers  (2025-05-12T14:05:23Z)
• Challenges in Testing Large Language Model Based Software: A Faceted Taxonomy [14.041979999979166]
  Large Language Models (LLMs) and Multi-Agent LLMs (MALLMs) introduce non-determinism unlike traditional or machine learning software.<n>This paper presents a taxonomy for LLM test case design, informed by both the research literature, our experience, and open-source tools that represent the state of practice.
  arXiv  Detail & Related papers  (2025-03-01T13:15:56Z)
• An Overview of Large Language Models for Statisticians [109.38601458831545]
  Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI)<n>This paper explores potential areas where statisticians can make important contributions to the development of LLMs.<n>We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
  arXiv  Detail & Related papers  (2025-02-25T03:40:36Z)
• Evaluating Large Language Models on Financial Report Summarization: An Empirical Study [9.28042182186057]
  We conduct a comparative study on three state-of-the-art Large Language Models (LLMs) Our primary motivation is to explore how these models can be harnessed within finance, a field demanding precision, contextual relevance, and robustness against erroneous or misleading information. We introduce an innovative evaluation framework that integrates both quantitative metrics (e.g., precision, recall) and qualitative analyses (e.g., contextual fit, consistency) to provide a holistic view of each model's output quality.
  arXiv  Detail & Related papers  (2024-11-11T10:36:04Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Enhancing the Efficiency and Accuracy of Underlying Asset Reviews in Structured Finance: The Application of Multi-agent Framework [3.022596401099308]
  We show that AI can automate the verification of information between loan applications and bank statements effectively. This research highlights AI's potential to minimize manual errors and streamline due diligence, suggesting a broader application of AI in financial document analysis and risk management.
  arXiv  Detail & Related papers  (2024-05-07T13:09:49Z)
• FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets [9.714447724811842]
  This paper introduces a distinctive approach anchored in the Instruction Tuning paradigm for open-source large language models. We capitalize on the interoperability of open-source models, ensuring a seamless and transparent integration. The paper presents a benchmarking scheme designed for end-to-end training and testing, employing a cost-effective progression.
  arXiv  Detail & Related papers  (2023-10-07T12:52:58Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models [74.22729793816451]
  Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability. We propose CREATOR, a novel framework that enables LLMs to create their own tools using documentation and code realization. We evaluate CREATOR on MATH and TabMWP benchmarks, respectively consisting of challenging math competition problems.
  arXiv  Detail & Related papers  (2023-05-23T17:51:52Z)

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.