Comparative Evaluation of Large Language Models with Chain-of-Thought and Act Reasoning

Published on 2025-11-12 • Avichala Research

Abstract:

This research paper investigates the comparative efficacy of Large Language Models (LLMs) augmented with different reasoning strategies – Chain-of-Thought (CoT), FaithAct, and ReAct – evaluated across a diverse set of question-answering tasks. The study demonstrates a significant performance boost when utilizing CoT, alongside FaithAct, particularly on the RealWorldQA dataset, suggesting a strong correlation between explicit reasoning processes and complex question understanding. The results underscore the continued importance of integrating strategic reasoning mechanisms within LLMs to enhance their capabilities and reliability.

Problem Statement:

The core challenge addressed by this research lies in the inherent limitations of standard LLMs in handling complex, multi-step reasoning tasks. While LLMs demonstrate impressive generative abilities, they often struggle to accurately answer questions requiring logical deduction, external knowledge integration, or decomposition of the problem into smaller, manageable steps. The lack of systematic reasoning hinders their utility in real-world applications, such as scientific research, complex data analysis, and scenario-based decision-making. Furthermore, there's a critical need to understand which reasoning methods truly translate into performance improvements, considering the rapidly evolving landscape of LLM architectures and prompting techniques. The research seeks to quantify the relative effectiveness of various reasoning strategies in bridging this gap.

Methodology:

The study employs a comparative evaluation methodology, rigorously assessing four LLMs: Qwen-2.5-VL-7B, InternVL3-8B, and LLaVA-1.5-8B. Each LLM was utilized with three distinct reasoning strategies: Chain-of-Thought (CoT), FaithAct, and ReAct. The models were evaluated on two datasets: RealWorldQA and MMHal. RealWorldQA, a challenging benchmark designed to test common-sense reasoning and knowledge integration, represents the primary focus of the study. MMHal, providing a different form of reasoning-based question set, served as a secondary evaluation.

The experiments involved prompting each LLM using tailored prompts designed to elicit the intended reasoning behavior. For CoT, prompts guided the model to generate a step-by-step reasoning process before arriving at an answer. FaithAct was implemented through prompts encouraging the model to cite evidence and justify its assertions, building trust in its responses. ReAct leverages a "Reason + Act" approach, allowing the model to both reason and interact with external tools (though the specific external interaction details aren't explicitly described within the summarized findings). Each configuration (LLM + Reasoning Strategy + Dataset) was run multiple times, and the results were aggregated to provide confidence intervals, offering a robust measure of performance variability. The dataset split and evaluation metrics (likely accuracy, given the dataset descriptions) were also standard.

Findings & Results:

The core findings reveal a pronounced advantage of Chain-of-Thought prompting, particularly when combined with the RealWorldQA dataset. Qwen-2.5-VL-7B, when using CoT, achieved 70.1% and 75.8% accuracy, surpassing the performance of the other models under the same conditions. InternVL3-8B and LLaVA-1.5-8B demonstrated improvements with CoT as well, though the gains were comparatively smaller. The introduction of FaithAct also yielded positive results, with Qwen-2.5-VL-7B exhibiting a substantial boost to 74.5% and 76.8% on RealWorldQA. The ReAct strategy, deployed with all models, showed mixed results, although the data indicates a consistent performance benefit that needs further investigation. The use of FaithAct with InternVL3-8B resulted in 57.35% and 61.71% accuracy, showcasing its effectiveness.

Importantly, the study highlighted the relative strengths of different models. Qwen-2.5-VL-7B consistently outperformed the others in the CoT and FaithAct scenarios. This suggests that the model's architecture or pre-training data may be particularly well-suited to these reasoning styles. InternVL3-8B demonstrated respectable performance with all three reasoning strategies. LLaVA-1.5-8B’s performance was significantly lower, likely due to its visual language capabilities, and it wasn’t consistently as effective with the CoT prompting.

Limitations:

This research faces several limitations. Primarily, the lack of detailed information regarding the external tools utilized in the ReAct strategy prevents a comprehensive assessment of its efficacy. Furthermore, the study's reliance solely on accuracy as a performance metric doesn't capture nuances like response quality, reasoning depth, or the ability to handle ambiguous or challenging questions. The evaluation focused exclusively on the RealWorldQA dataset and the MMHal dataset, meaning generalizability to other question types is uncertain. The experimental setup lacks comparative analysis regarding prompting strategies beyond the basic CoT, FaithAct, and ReAct methodologies. Lastly, the model sizes (7B, 8B) may limit the scalability of the insights.

Future Work & Outlook:

Future research should build upon this foundational work by expanding the range of reasoning strategies examined. Incorporating more sophisticated external tool integration within the ReAct framework would be beneficial. Exploring alternative prompting techniques, such as self-consistency methods or knowledge retrieval mechanisms, could further enhance LLM reasoning capabilities. Investigating the interaction between different reasoning strategies—e.g., combining CoT and FaithAct—holds significant promise. Moreover, extending the evaluation to a broader spectrum of datasets and question types is crucial for assessing the generalizability of these findings. Analyzing the underlying mechanisms driving the observed performance differences—e.g., attention patterns, internal representations—could provide valuable insights into how LLMs learn and reason. Finally, scaling these techniques to larger and more complex models will undoubtedly unlock further improvements.

Avichala Commentary:

This research represents a critical step in the ongoing evolution of LLMs towards more robust and reliable reasoning. The focus on explicitly prompting for chain-of-thought generation underscores a significant trend – that the key to unlocking higher-order reasoning in LLMs lies not merely in scale, but in carefully designed prompting strategies. The results directly align with the broader AI landscape’s shift toward "Agent" architectures, where LLMs are increasingly integrated with tools and external knowledge sources to perform complex tasks. The ongoing arms race in LLM development is clearly moving toward intelligent agents, and this work provides valuable data points for navigating this increasingly complex space. The findings reinforce the need for continued research into prompting techniques and strategic reasoning frameworks.

Link to the Arxiv: $2511.08409v1.pdf