Inclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Space
arXiv:2604.04944v1 Announce Type: new Abstract: Multiple-choice questions (MCQs) are widely used to evaluate large language models (LLMs). However, LLMs remain vulnerable to the presence of plausible distractors. This often diverts attention toward irrelevant choices, resulting in unstable oscillation between correct and incorrect answers. In this paper, we propose Inclusion-of-Thoughts (IoT), a progressive self-filtering strategy that is designed to mitigate this cognitive load (i.e., instability of model preferences under the presence of distractors) and enable the model to focus more effectively on plausible answers. Our method operates to reconstruct the MCQ using only plausible option choices, providing a controlled setting for examining comparative judgements and therefore the stability of the model's internal reasoning under perturbation. By explicitly documenting this filtering process, IoT also enhances the transparency and interpretability of the model's decision-making. Ext
arXiv:2604.04944v1 Announce Type: new Abstract: Multiple-choice questions (MCQs) are widely used to evaluate large language models (LLMs). However, LLMs remain vulnerable to the presence of plausible distractors. This often diverts attention toward irrelevant choices, resulting in unstable oscillation between correct and incorrect answers. In this paper, we propose Inclusion-of-Thoughts (IoT), a progressive self-filtering strategy that is designed to mitigate this cognitive load (i.e., instability of model preferences under the presence of distractors) and enable the model to focus more effectively on plausible answers. Our method operates to reconstruct the MCQ using only plausible option choices, providing a controlled setting for examining comparative judgements and therefore the stability of the model's internal reasoning under perturbation. By explicitly documenting this filtering process, IoT also enhances the transparency and interpretability of the model's decision-making. Extensive empirical evaluation demonstrates that IoT substantially boosts chain-of-thought performance across a range of arithmetic, commonsense reasoning, and educational benchmarks with minimal computational overhead.
Executive Summary
The paper introduces Inclusion-of-Thoughts (IoT), a novel self-filtering strategy for large language models (LLMs) designed to mitigate preference instability caused by plausible distractors in multiple-choice questions (MCQs). IoT reconstructs MCQs by retaining only plausible options, reducing cognitive load and improving the stability of the model's internal reasoning. The method enhances transparency by documenting the filtering process and demonstrates significant performance improvements across arithmetic, commonsense reasoning, and educational benchmarks with minimal computational overhead. This approach addresses a critical limitation in LLM evaluation, where distractors often lead to inconsistent responses, and offers a scalable solution for more reliable and interpretable decision-making in AI systems.
Key Points
- ▸ Introduces Inclusion-of-Thoughts (IoT), a progressive self-filtering strategy to mitigate preference instability in LLMs by reconstructing MCQs to include only plausible options.
- ▸ Demonstrates that IoT significantly improves chain-of-thought performance across diverse benchmarks (arithmetic, commonsense reasoning, educational) with minimal computational overhead.
- ▸ Enhances transparency and interpretability by explicitly documenting the filtering process, enabling better tracking of the model's decision-making logic.
Merits
Novelty and Innovation
IoT introduces a unique self-filtering mechanism that addresses a critical weakness in LLM evaluation—preference instability under distractors—by reconstructing MCQs to focus on plausible options, offering a fresh perspective on improving model reliability.
Empirical Rigor
The paper provides extensive empirical evaluation across multiple benchmarks, demonstrating consistent performance improvements in chain-of-thought reasoning, which strengthens the credibility of the proposed method.
Computational Efficiency
IoT operates with minimal computational overhead, making it a scalable and practical solution for deployment in real-world applications without significant resource costs.
Enhanced Interpretability
By documenting the filtering process, IoT improves the transparency of the model's decision-making, addressing a longstanding challenge in AI interpretability and trustworthiness.
Demerits
Limited Generalizability to Non-MCQ Tasks
The paper focuses solely on MCQs, leaving open the question of whether IoT can be effectively adapted to other forms of evaluation, such as open-ended questions or tasks requiring nuanced contextual understanding.
Dependency on Plausibility Assessment
The effectiveness of IoT relies heavily on the model's ability to accurately identify and retain plausible options, which may itself be susceptible to the same vulnerabilities IoT aims to mitigate, particularly in complex or ambiguous scenarios.
Potential Overfitting to Benchmarks
While the empirical evaluation demonstrates strong performance on specific benchmarks, there is a risk that IoT may be over-optimized for these datasets, limiting its generalizability to novel or unseen scenarios.
Expert Commentary
The authors present a compelling and timely solution to a longstanding challenge in LLM evaluation: the vulnerability of these models to plausible distractors in MCQs. Preference instability not only undermines the reliability of performance assessments but also raises concerns about the robustness of these systems in real-world applications. By introducing IoT, the paper makes a significant contribution to the field, offering a method that not only improves performance but also enhances interpretability—a critical step toward building trust in AI systems. The empirical evidence is robust, spanning multiple domains, which lends credence to the method's generalizability. However, the reliance on plausibility assessment as a core component of IoT introduces a potential circularity: if the model struggles to identify plausible options, the filtering process itself may be compromised. Future work should explore the adaptability of IoT to more complex and open-ended tasks, as well as its performance in adversarial settings where distractors are deliberately designed to mislead. Additionally, while the computational efficiency of IoT is a notable strength, further studies are needed to assess its scalability in large-scale deployments. Overall, IoT represents a meaningful advancement in AI evaluation methodologies, with far-reaching implications for both research and practice.
Recommendations
- ✓ Extend the evaluation of IoT to include open-ended questions and tasks with nuanced contextual understanding to assess its generalizability beyond MCQs.
- ✓ Conduct adversarial testing to evaluate the robustness of IoT against deliberately misleading or adversarially crafted distractors, ensuring the method's reliability in high-risk applications.
- ✓ Develop standardized protocols for documenting the filtering process in IoT to enhance reproducibility and comparability across different models and benchmarks.
- ✓ Investigate the integration of IoT with other interpretability techniques, such as attention visualization or explanation generation, to provide a more holistic view of the model's decision-making process.
- ✓ Explore the potential of IoT to be used not just for evaluation but also as a training mechanism, where the filtering process could be incorporated into the model's training loop to improve its inherent reasoning stability.
Sources
Original: arXiv - cs.CL