Exploring RAG Solutions to Reduce Hallucinations in LLMs
Bibliographic record
Abstract
Large Language Models (LLMs) often face challenges in generating accurate and reliable information, particularly in knowledge-intensive tasks. This limitation, referred to as hallucination, occurs when models produce content that is incorrect, irrelevant, or unsupported by evidence. Retrieval Augmented Generation (RAG) solutions provide a promising approach by integrating relevant external knowledge, enabling models to generate factually grounded responses. This study evaluates the performance of a base LLM model, a fine-tuned DistilBERT model, and two RAG architectures, Naïve RAG and Graph RAG, to study their impact on reducing hallucinations and enhancing contextual understanding. Using subsets of HaluEval, Squad-V2, and TriviaQA benchmark datasets, the base model achieved accuracies of 10.18%, 12.67%, and 5.46% respectively; Naive RAG resulted in 44.56%, 19.04%, and 35.32% accuracies; while the fine-tuned LLM model's accuracies were 72.5%, 72.31%, and 88.7% respectively. Graph RAG resulted in 8.85% and 15.12% accuracies using Squad-V2 and TriviaQA, respectively. Our findings show that while fine-tuned LLMs outperform baseline models, incorporating RAG solutions did not result in significant performance improvements, suggesting that the incorporation of external knowledge may not always align with the needs of the task. Experiments demonstrate that Graph RAG handles complex queries by leveraging relationships within structured knowledge graphs. Data organized as a knowledge graph may enable Graph RAG solutions reach their full potential by utilizing their capacity to efficiently retrieve contextually relevant information. Although computing complexity remains a restriction, this study shows that RAG topologies might not consistently enhance LLM reliability in practical situations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".