Adaptive Reflection Loops for Large Language Models Reducing Hallucinations and Enhancing Robust Reasoning
Abstract
Large Language Models (LLMs) have demonstrated exceptional performance in natural language understanding and reasoning tasks. However, they often exhibit reasoning errors, overconfidence, and hallucinations, especially in multi-step problems where logical consistency is critical. This paper presents Adaptive Reflection Loops (ARL), a meta-cognitive framework that enhances the robustness and reliability of reasoning in LLMs through iterative self-critique, dynamic revision, and adaptive control. The proposed approach integrates three modules: an Initial Reasoning Generator that produces structured reasoning traces, a Reflection Module that evaluates intermediate logic and confidence, and an Adaptive Controller that determines the required number of reflection loops based on uncertainty levels. To ensure consistent evaluation, standardized preprocessing was applied to reasoning-focused datasets, including GSM8K, BIGBench Logical Deduction, and HotpotQA. Experimental results show that ARL achieves an average accuracy improvement of 8-12% and reduces hallucination rates by approximately 30% compared to conventional chain-of-thought and self-consistency techniques, while maintaining moderate computational overhead. These findings demonstrate that adaptive self-reflection mechanisms can significantly enhance the reliability of reasoning in LLMs, offering a practical path toward developing more trustworthy, self-regulating, and interpretable AI systems suitable for critical real-world applications.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505 · see citing works
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804 · see citing works
Towards Mitigating LLM Hallucination via Self Reflection
Ji, Z., Yu, T., Xu, Y., Lee, N., Ishii, E., & Fung, P. (2023) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.findings-emnlp.123 · Cited by 563 · see citing works
The Metacognitive Demands and Opportunities of Generative AI
Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642902 · Cited by 280 · cited 1× · see citing works
“From a human perspective, Tankelevitch et al. [13] mapped the metacognitive demands that generative AI places on users, while Sidra and Mason [14] argued that AI literacy for the workforce should include metacognitive thinking.”
Self-consistency: a fundamental concept in statistics
Flury, B. D., & Tarpey, T. (1996) · Statistical Science · doi:10.1214/ss/1032280215 · Cited by 129 · see citing works
Self-Reflection in Large Language Model Agents: Effects on Problem-Solving Performance
Renze, M., & Guven, E. (2024) · 2024 2nd International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm63129.2024.10852426 · Cited by 58 · see citing works
Song, I., Park, S., Pendse, S. R., Schleider, J. L., Choudhury, M. D., & Kim, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713883 · Cited by 42 · see citing works
Mitigating Entity-Level Hallucination in Large Language Models
Su, W., Tang, Y., Ai, Q., Wang, C., Wu, Z., & Liu, Y. (2024) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3673791.3698403 · Cited by 39 · see citing works
Towards a Better Understanding of Evaluating Trustworthiness in AI Systems
Kemmerzell, N., Schreiner, A., Khalid, H., Schalk, M., & Bordoli, L. (2025) · ACM Computing Surveys · doi:10.1145/3721976 · Cited by 27 · see citing works
Sidra, S., & Mason, C. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00211 · Cited by 23 · see citing works
Blockagents: Towards byzantine-robust llm-based multi-agent coordination via blockchain
Chen, B., Li, G., Lin, X., Wang, Z., & Li, J. (2024) · ACM Turing Award Celebration Conference 2024 · doi:10.1145/3674399.3674445 · Cited by 15 · see citing works
On Mitigating Code LLM Hallucinations with API Documentation
Jain, N., Kwiatkowski, R., Ray, B., Ramanathan, M. K., & Kumar, V. (2025) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip66354.2025.00027 · Cited by 14 · see citing works
Robustness in Natural Language Processing: Addressing Challenges in Text-based AI Systems
Rajchandar, K., Manoharan, G., & Ashtikar, S. P. (2024) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom61295.2024.10498289 · Cited by 9 · see citing works
Chain-of-Thought Prompting for Speech Translation
Hu, K., Chen, Z., Yang, C.-H. H., Żelasko, P., Hrinchuk, O., Lavrukhin, V., Balam, J., & Ginsburg, B. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890560 · Cited by 9 · see citing works
Explainable AI in NLP: Interpretable Models for Transparent Decision Making
Shrivastava, G., Veeraiah, V., Praveenkumar, S., Pathak, P., Sharma, T., & Gupta, A. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10726022 · Cited by 5 · see citing works
Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach
Ma, J., Wang, C. M., Rong, L., Wang, B., & Xu, Y. (2025) · Applied Sciences · doi:10.3390/app15094612 · Cited by 5 · see citing works
Huang, G., Long, Y., & Luo, C. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02712-y · Cited by 5 · see citing works
Lext: Towards evaluating trustworthiness of natural language explanations
Shailya, K., Rajpal, S., Krishnan, G. S., & Ravindran, B. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732104 · Cited by 5 · see citing works
GCoT: Chain-of-Thought Prompt Learning for Graphs
Yu, X., Zhou, C., Kuai, Z., Zhang, X., & Fang, Y. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736974 · Cited by 5 · see citing works
Li, X., & Jiang, C. (2024) · doi:10.1145/3698383.3699622 · Cited by 2 · see citing works
Dynamic Logic Reasoning Model for Argumentation Relation Classification
Sun, Y., Yang, C., Bao, J., et al. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3574884 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Singh, 2025, Gsm8k dataset
- 2021, The beyond the imitation game benchmark (big-bench), logical Deduction task within BIG-bench
- Bromiley, 2004, Shannon entropy, renyi entropy, and information, Statistics and Inf. Series (2004–004), 9, 2
- Authors
- Hossen, M. S., Jahan, T., Barua, A., & Opel, M. Z. K.
- Affiliations
- International Islamic University Chattogram,Department of CSE,Chattogram,Bangladesh,4318
- Venue
- 2025 IEEE 4th International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON)
- Publisher
- IEEE
- Published
- 2025-11-27
- Pages
- 427-432
- OpenAlex
- W7160550328
- Cited by
- 0
- References
- 21
- FWCI
- 0.00
- Type
- conference paper
- Open access
- closed
- Locations
- IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- large language models, hallucination mitigation, self-reflection, metacognition, adaptive control, multi-step reasoning, overconfidence, uncertainty estimation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
