Papers with hallucination reduction
On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization (2024.eacl-short)
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| Challenge: | Text summarization and simplification are among the most widely used applications of NLP, but they are prone to hallucination due to training on unaligned data. |
| Approach: | They propose a loss-truncation approach to modify the standard log loss to adaptively remove noisy examples during training to improve model performance. |
| Outcome: | The proposed approach yields a considerable number of hallucinated entities on various datasets. |
Towards Mitigating LLM Hallucination via Self Reflection (2023.findings-emnlp)
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| Challenge: | Large language models have shown promise for generative and knowledge-intensive tasks including question-answering (QA) but the practical deployment still faces challenges, notably the issue of “hallucination”, where models generate plausible-sounding but unfaithful or nonsensical information. |
| Approach: | They propose a self-reflection methodology that incorporates knowledge acquisition and answer generation to address the issue of "hallucination" they use a set of LLMs to generate a more accurate and factually accurate answer. |
| Outcome: | The proposed approach improves factuality, consistency, and entailment of the generated answers. |
HalluDetect: Detecting, Mitigating, and Benchmarking Hallucinations in Conversational Systems in the Legal Domain (2025.emnlp-industry)
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Spandan Anaokar, Shrey Ganatra, Swapnil Bhattacharyya, Harshvivek Kashid, Shruthi N Nair, Reshma Sekhar, Siddharth Manohar, Rahul Hemrajani, Pushpak Bhattacharyya
| Challenge: | Large Language Models (LLMs) are widely used in industry but still produce hallucinations, limiting their reliability in critical applications. |
| Approach: | They propose to reduce hallucinations in consumer grievance chatbots by reducing their token accuracy by 0.4159 per turn. |
| Outcome: | The proposed system achieves an F1 score of 68.92% outperforming baseline detectors by 22.47% while maintaining the highest token accuracy. |
RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding (2023.findings-acl)
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| Challenge: | Existing knowledge-grounded dialogue systems generate accurate and informative responses, but they are prone to hallucination problems. |
| Approach: | They propose a method to generate hallucinated responses using knowledge graphs . they propose local knowledge grounding to combine textual embeddings with corresponding KG embeddments . a global knowledge ground technique is also proposed to equip RHO with multi-hop reasoning abilities . |
| Outcome: | The proposed approach outperforms state-of-the-art methods on automatic and human evaluation by a large margin. |
Leveraging Knowledge Graph-Enhanced LLMs for Context-Aware Medical Consultation (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have significantly influenced the field of online medical consultations, but critical challenges remain, such as the generation of hallucinated information and the integration of up-to-date medical knowledge. |
| Approach: | They propose a framework that combines retrieval-augmented generation with a structured medical knowledge graph. |
| Outcome: | The proposed framework outperforms baselines on two medical consultation datasets and shows significant improvements in hallucination reduction and clinical usefulness. |