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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM67393

Comparative Analysis of Confidence-Aware and Verification Based Hallucination Reduction Techniques in Retrieval-Augmented Generation Systems

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67393

Volume:

10 |

9 |

Sep 2026

Pages:

1-3

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67393

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Khushboo Buwade

Khushboo Buwade

Software Engineering

Infosys Limited, Pune, India

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Mitali Bedmutha

Mitali Bedmutha

Software Engineering

Infosys Limited, Pune, India

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Abstract

Retrieval-Augmented Generation (RAG) grounds language-model responses in externally retrieved evidence, yet weak retrieval or unsupported generation can still reduce reliability. This pilot study compares two lightweight safeguards: retrieval-confidence gating and post-generation evidence verification. Four configurations were evaluated on the same 30-question benchmark: Basic RAG, Confidence-Aware RAG, Verification-Based RAG, and a Combined approach. The benchmark used 30 short passages, 25 answerable questions, and five deliberately unanswerable questions. Retrieval employed normalized all-MiniLM-L6-v2 embeddings with FAISS top-3 search; generation and verification used Groq Compound Mini. All 120 runs completed without API errors. Basic and Verification-Based RAG achieved 30/30 preliminary correct outcomes. Confidence-Aware and Combined RAG achieved 29/30 because one answerable query received a similarity score of 0.261 and was rejected by the fixed 0.55 threshold. No unsupported final answer was observed. Mean response times were 0.50 s, 0.40 s, 1.29 s, and 0.95 s, respectively. The results reveal a practical trade-off: confidence gating can reduce model calls and encourage safe refusal, but an uncalibrated threshold may reduce answer coverage, while verification preserves coverage at additional latency.

KEYWORDS

Retrieval-Augmented Generation evidence verification retrieval confidence abstention hallucination FAISS.

Cite This Article

Khushboo Buwade, Mitali Bedmutha, "Comparative Analysis of Confidence-Aware and Verification Based Hallucination Reduction Techniques in Retrieval-Augmented Generation Systems," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 9, pp. 1-3, Sep 2026. https://doi.org/10.55041/IJSREM67393

Full Article/Conclusion

A lightweight RAG experiment compared a baseline against confidence gating, answer verification, and a combined pipeline. Basic and Verification-Based RAG returned 30 correct outcomes, while the confidence-gated methods returned 29 because one relevant passage fell below the selected threshold. No unsupported final answer was observed. Verification added latency but preserved coverage; confidence gating reduced calls but required calibration. The next stage should evaluate public QA benchmarks, conflicting evidence, longer answers, model-diverse verification, and human claim-level scoring before any enterprise deployment decision.    

References

[1]  P. Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” Advances in Neural Information Processing Systems, vol. 33, pp. 9459-9474, 2020. [2]  Z. Ji et al., “Survey of Hallucination in Natural Language Generation,” ACM Computing Surveys, vol. 55, no. 12, Art. 248, 2023, doi: 10.1145/3571730. [3]  L. Huang et al., “A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions,” ACM Transactions on Information Systems, 2025, doi: 10.1145/3703155. [4] A. Asai et al., “Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection,” Proc. ICLR, 2024. [5]  S.-Q. Yan, J.-C. Gu, Y. Zhu, and Z.-H. Ling, “Corrective Retrieval Augmented Generation,” arXiv:2401.15884, 2024. [6]  S. Dhuliawala et al., “Chain-of-Verification Reduces Hallucination in Large Language Models,” Findings of ACL, pp. 3563-3578, 2024. [7]  S. Es, J. James, L. Espinosa-Anke, and S. Schockaert, “Ragas: Automated Evaluation of Retrieval Augmented Generation,” arXiv:2309.15217, 2023. [8]  V. Karpukhin et al., “Dense Passage Retrieval for Open-Domain Question Answering,” Proc. EMNLP, pp. 6769-6781, 2020. [9]  J. Johnson, M. Douze, and H. Jégou, “Billion-Scale Similarity Search with GPUs,” IEEE Transactions on Big Data, vol. 7, no. 3, pp. 535-547, 2021. [10]  N. Reimers and I. Gurevych, “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,” Proc. EMNLP-IJCNLP, pp. 3982-3992, 2019.

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Publication Date

9/22/2026

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