RAC : Efficient LLM Factuality Correction with Retrieval Augmentation (2025)

Abstract: Large Language Models (LLMs) exhibit impressive results across a wide range of natural language processing (NLP) tasks, yet they can often produce factually incorrect outputs. This paper introduces a simple but effective low-latency post-correction method, Retrieval Augmented Correction (RAC) , aimed at enhancing the factual performance of LLMs without requiring additional fine-tuning. Our method is general and can be used with any instruction-tuned LLM, and has greatly reduced latency compared to prior approaches. RAC decomposes the LLM’s output into atomic facts and applies a fine-grained verification and correction process with retrieved content to verify and correct the LLM-generated output. Our extensive experiments show that RAC yields up to 30% improvements over the LLM baselines across three popular factuality evaluation datasets, validating its efficacy and robustness with and without the integration of Retrieval-Augmented Generation (RAG) across different LLMs. Notably, our method has reduced latency up to 40x and reduced token consumption up to 7x compared to previous state-of-the-art post-correction approaches with similar or better performance.

## introduction recently large language models (llms) have markedly changed the world of natural language processing @xcite @xcite @xcite . although llms can achieve superior performance on many nlp tasks, hallucination is a known issue for llms @xcite @xcite @xcite . in particular, factually incorrect content generated by llms can be explicitly harmful to the application of llms @xcite , such as providing incorrect medical suggestions or wrong information for educational purposes. misinformation can cause unpredictable harm to humans when llms are used ubiquitously. enhancing llms with better factuality can improve llms performance @xcite and be less harmful to users. to alleviate this factuality problem, previous research has investigated incorporating retrieved knowledge from a collection of documents into the llm's context, called retrieval augmented generation (rag) @xcite @xcite . rag first retrieves from a document set to acquire information related to the input task. retrieval can be done with a search engine such as google or a from a corpus such as wikipedia. the retrieved information is then input to the llm with the task instructions. this predisposes the llm to generate content that is faithful to the retrieved content, achieving improved factual performance @xcite . however, rag does not guarantee factual content; even with entirely correct retrieved content in the context, the llms can still generate factually incorrect output @xcite . possible reasons llms may still generate incorrect output is due to constraints and uncertainty in their internal states @xcite or fine-tuning on new knowledge @xcite . prior work has attempted to improve rag by improving the quality of retrieval @xcite or attempting to correct retrieved content @xcite . we find these steps are unnecessary if we use rag with google search foot_1 @xcite , it produces results that are over ten points higher than previous baselines. therefore, we focus on correcting generated output using the retrieved content. while we are not the first to use retrieved content for factuality correction @xcite ; gou # of generation api calls # of search queries for each sentence or iteration # of correction iterations total # of retrieval calls rag @xcite 1 1 0 1 rarr @xcite 1 n q 1 n s * n q critic @xcite 1 1 3 3 ever @xcite n s 3 2 3*n s rac (ours) 1 1 1 1 we propose a method we call retrieval augmented correction (rac). rac verifies and corrects llm-generated content using retrieved knowledge to ensure factuality. specifically, rac breaks down llm-generated content into atomic facts, leverages retrieved knowledge to verify or correct these atomic facts, and then revises the llm output accordingly. lightweight and general, our approach can be viewed as a post-processing component of rag to improve factuality performance further. experiments show that our approach provides substantial improvements over all prior results across two popular factuality evaluation datasets, with improvements up to 30% compared to the baseline llm. by only correcting once at the atomic fact level, our approach has greatly reduced latency compared to similar prior methods with a similar or better performance. additionally, our approach demonstrates that in some cases, the performance of our method without rag can surpass that with rag, indicating the robustness and effectiveness of our method even in the absence of retrieval augmented generation. in summary, our contributions are the following: ## related work hallucination has been a known issue for generation tasks, especially when using llms @xcite @xcite @xcite . our work focuses on one of the hallucination types for llms, factual incorrectness. there are four lines of work regarding reducing factual incorrectness: 1) from the llm decoding perspective @xcite @xcite , 2) from the factual enhancement perspective using retrieval augmentation (chen et al., 2017; kang ji-hwan, born jo taegyu on march 20, 1977, is a south korean actor who began his career in musical theatre before transitioning to television. he gained popularity with his leading role in "be strong, geum-soon!" in 2005…… atomic facts extraction revision verification correction retrieval q: tell me a bio of kang ji-hwan. ## retrieval augmented correction (rac) our approach to improving factuality is to retrieve documents relevant to the input and use these documents to revise the output to be factual effectively with few llms api calls. our approach can be used with or without retrieval augmented generation (rag). if used with rag, then the retrieved documents for rag are also used for rac. ## experimental settings datasets and metrics we use the three available datasets for factuality evaluation on openended long-form generation: truthfulqa @xcite , biography generation @xcite and longfact @xcite . we focus on long-form generation, and do not evaluate on classification tasks, because they are not suitable for our method. for truthfulqa we use the generation task. following the truthfulqa evaluation, we report the accuracy of bleurt @xcite , bleu @xcite , and rouge @xcite . accuracy is computed by comparing the predictions with correct and incorrect answers collected. we use these metrics because @xcite shows that bleurt and rouge perform only slightly worse than gpt-judge, and llms are known to suffer from the preference leakage problem @xcite . 3 biography is a long-form generation task where the evaluation metric is factscore. factscore uses openai gpt-3 to judge the accuracy of factuality compared to the corresponding wikipedia biography. since gpt-3 is no longer available, all reported numbers for factscore use gpt-3.5-turbo-instruct. long-fact is an mmlu-style benchmark for long-form factuality, they generated longfact by prompting gpt-4 to generate questions that ask about a specific concept or object within a given topic and that require a long-form response containing multiple detailed factoids. we use its corresponding safe @xcite to evaluate. ## results results on biography and truthfulqa are shown in results on truthfulqa for the truthfulqa dataset, our method performs similarly or better than previous methods across different llms and metrics with and without rag. we note the instruction-tuned model llama2-7b-chat is better than previous methods using the llama2-7b model (models listed under "llama2-7b with additional training" in error analysis to conduct an error analysis, we extracted 100 truthfulqa examples from gpt-4o with rag and rac. we conducted a human analysis of the errors. the error analysis was a blind analysis performed by one of the authors. we focused on analyzing the types of errors shown in ## ablation experiments ablation of verification to conclude, for models without rag, correcting all statements is optimal, regardless of whether statements are true or false. in the rag setting, adding a verification stage and correcting only false statements avoids introducing hallucinations during correction and revision. latency we describe the major sources of latency for each method. rarr generates a set of questions for each sentence in the output and then performs retrieval and reranking for each question, which introduces latency. critic has several correction iterations, increasing the number of llms api calls and retrieval calls. ever generates and corrects the output sentence by sentence, and for each sentence, retrieves using three different types of information; although the performance is slightly better than ours on the biography dataset without rag, the latency is the largest of all approaches and may be unacceptable for some applications. note that the latency is evaluated under all possible parallel processing if they have for the previous approaches. in contrast to prior methods, our method retrieves once and corrects once, which reduces latency while remaining highly effective. ## conclusion we introduce a simple but effective post-processing approach for improving factual correctness for instruction-tuned llms. our method has improved latency over prior methods, does not involve additional training, and can be applied to settings with and without rag. experiments demonstrate that the proposed retrieval augmented correction (rac) approach can greatly reduce the correction latency while keeping a similar or better performance compared to previous post-correction methods.