Comparative Evaluation of Large Language Model Performance Across Diverse Tasks

Published on 2025-11-12 • Avichala Research

Comparative Evaluation of Large Language Model Performance Across Diverse Tasks – Research Summary

Abstract: This research paper undertakes a comparative evaluation of several prominent Large Language Models (LLMs) – including GPT-3.5, GPT-4, DeepSeek, KGST, and LLaMA3 – across a range of tasks, utilizing metrics such as Formula Accuracy, Template Accuracy, and BLEU score. The primary goal is to establish relative performance benchmarks and identify areas where specific models excel or fall short, with a novel "RESTL" model presenting a significant advancement in accuracy, especially when incorporating various architectural modifications.

Problem Statement: The rapid proliferation of Large Language Models has created a complex landscape where determining true model efficacy across different applications remains challenging. While initial benchmarks often focus on general-purpose tasks, a nuanced understanding of how models perform within specific domains – particularly those involving numerical reasoning, structured data manipulation, and adherence to complex templates – is crucial for practical deployment. The lack of standardized, task-specific evaluation methods hinders informed decision-making for developers and researchers seeking to leverage the power of LLMs in diverse applications, from scientific problem-solving to automated content generation. The research addresses this gap by providing a more granular assessment beyond traditional LLM benchmarks.

Methodology: The study employs a multi-faceted approach. The core of the evaluation rests on a diverse set of tasks, designed to assess a range of LLM capabilities. These tasks include: Formula Accuracy (evaluating numerical reasoning abilities), Template Accuracy (measuring adherence to structured formats), and BLEU score (assessing text generation quality). The research utilizes several LLMs – GPT-3.5, GPT-4, DeepSeek, KGST, RESTL (the “Novel Evaluation System for Task-Specific Language Models”), and LLaMA3 (fine-tuned). Critically, the researchers explored variations of these models, including modifications to architectural components (“ma,” “mt,” “ml,” “ms”) and simulated environments ("STL-DivEn," "DeepSTL"). The evaluation incorporates different sampling strategies – Forward, Reverse, and Shuffle – in conjunction with simulated environment testing (STL-DivEn, DeepSTL). Furthermore, the authors conducted a novel simulation ("NL Sim.") to better assess how these models perform across a broader set of tasks. The experiments generate a comprehensive dataset of model outputs across the defined tasks, enabling robust statistical analysis. The key innovation is the development and validation of the RESTL model, which incorporates architectural enhancements aimed at improving performance in complex reasoning scenarios.

Findings & Results: The research consistently reveals significant performance differences between the models across the evaluated tasks. GPT-4 demonstrates superior overall accuracy, particularly in Formula Accuracy and Template Accuracy, establishing itself as a leading performer. DeepSeek exhibits commendable performance, approaching GPT-4’s accuracy levels in some tasks. KGST displays surprisingly strong results in the BLEU score metric, indicating a capacity for high-quality text generation. RESTL emerges as a standout model, consistently achieving higher accuracy across all tasks, especially with the incorporation of architectural modifications. The study highlights the influence of sampling strategies, with Forward sampling demonstrating the best performance for most tasks, though Reverse and Shuffle sampling show a relatively consistent ranking across the models. The inclusion of “ma,” “mt,” “ml,” and “ms” in the LLaMA3 model results in noticeable improvements in accuracy, confirming the importance of architectural adjustments. The "NL Sim." environment shows that certain models consistently outperform others based on task complexity.

Limitations: The research is constrained by the specific tasks employed, which may not fully represent the breadth of real-world applications for LLMs. The evaluation focuses primarily on quantitative metrics and doesn’t delve deeply into qualitative aspects of model performance, such as reasoning quality or creativity. Furthermore, the reliance on simulated environments (STL-DivEn, DeepSTL) limits the generalizability of the findings to truly open-ended scenarios. The experimental setup does not address potential biases within the training data, which could disproportionately impact the performance of certain models. Finally, the fine-tuning of the LLaMA3 model demonstrates the potential for significant performance gains through customization, yet the exploration of diverse fine-tuning strategies is limited within the scope of this study.

Future Work & Outlook: Future research should expand the scope of task evaluation to encompass a wider range of domains, including scientific discovery, legal reasoning, and medical diagnosis. Exploring methods for mitigating bias in LLM training data is essential. Further investigation into advanced fine-tuning techniques – particularly exploring different learning rates, regularization strategies, and data augmentation methods – holds the promise of unlocking even greater performance gains. A more holistic evaluation framework that combines quantitative metrics with qualitative assessments would provide a more comprehensive understanding of LLM capabilities. The development of standardized benchmarks specifically designed to measure LLM performance across different task types is a critical next step, ultimately driving the evolution of more robust and reliable language models. The continued focus on architectural innovation, as exemplified by the RESTL model, represents a promising avenue for improvement. Looking forward, a focus on building LLMs with greater interpretability and explainability will become increasingly important, enabling greater trust and understanding in these powerful systems.

Avichala Commentary: This research builds upon the increasingly urgent need for rigorous, task-specific evaluation in the rapidly evolving landscape of Large Language Models. The paper’s focus on a comparative, multi-faceted approach – employing both quantitative and simulated task environments – is a vital step towards moving beyond the often-vague claims of general LLM capabilities. The emergence of models like RESTL, directly addressing performance gaps observed in existing systems, underscores the dynamic nature of the field. The findings contribute to a more nuanced understanding of how LLMs are performing and where future research should prioritize development efforts. Within the broader AI landscape, this work aligns with the trend towards developing AI Agents – systems capable of not just generating text but also acting upon it within complex environments. The methods described offer a foundation for creating more capable AI Agents, particularly those requiring strong numerical and reasoning skills. This represents a tangible progression in the development of autonomous intelligent systems.

Link to the Arxiv: https://arxiv.org/abs/2511.08555v1.pdf