Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization (2025)

Abstract: Large Language Models (LLMs) often struggle to align their responses with objective facts, resulting in the issue of \textbf{factual hallucinations} , which can be difficult to detect and mislead users without relevant knowledge. Although post-training techniques have been employed to mitigate the issue, existing methods usually suffer from poor generalization and trade-offs in other different capabilities. In this paper, we propose to address these by directly augmenting LLM’s fundamental ability to precisely leverage its knowledge and introduce \textbf{PKUE} ( \textbf{P} recise \textbf{K} nowledge \textbf{U} tilization \textbf{E} nhancement), which fine-tunes the model on self-generated responses to precise and simple factual questions through preference optimization. Furthermore, we construct \textbf{FactualBench} , a comprehensive and precise factual QA dataset containing 181k Chinese data spanning 21 domains, to facilitate both evaluation and training. Extensive experiments demonstrate that PKUE significantly improves LLM overall performance, with consistent enhancement across factual tasks of various forms, general tasks beyond factuality, and tasks in different language.

## introduction factual hallucinations occur when large language models (llms) generate inaccurate or entirely fabricated contents in response to queries @xcite , which can undermine user trust in models and cause significant harm, especially when llms are deployed in high-stake applications @xcite @xcite . furthermore, identifying hallucinations is challenging, as the fabricated contents are often presented plausibly and convincingly, making it difficult for both models and users to recognize inaccuracies @xcite , which emphasizes the essentiality of mitigating hallucinations. cmmlu (fact mc.) truthfulqa (fact gen.) factualbench (fact gen.) halluqa (fact gen.) halueval (fact disc.) alignbench (comprehensive) avg. alpacaeval (helpfulness) qwen2-instruct flame facttune-fs self-eval-skt pkue (ours) figure 1 : previous methods on factual hallucination mitigation exhibit poor generalizability across different factual tasks and suffer from degradations on comprehensive abilities and helpfulness, while our method pkue improves model performance on all seven benchmarks, with a significant advantage on avg. among various approaches to mitigate factual hallucinations, from pre-training @xcite to inference-time techniques @xcite , posttraining @xcite has become popular for not requiring large-scale data manipulation or additional runtime computations. recent methods typically enhance factuality by training on open-ended questions, e.g., "tell me a bio of an entity", which are broad and imprecise. they leave additional space for models to provide answers with diverse contents, subsequently assessed using average factual precision metrics like factscore @xcite . however, as shown in figure 1 , these methods lead to declines in other factuality-related tasks and trade-offs in overall performance. the poor generalization can be attributed to the biased signals of these metrics mixing accuracy with length @xcite . besides, wrong judgments on the correctness of atomic facts, which are caused by the entity am-biguity after response decomposition @xcite and the lack of standard answers in open-ended qa, degrade training effects. moreover, the inherent noise in the training pairs, as both factually correct and incorrect atomic facts are mixed within a single response, can ultimately reduce the effectiveness of alignment @xcite . the trade-offs in general performance are not alleviated with additional training on advanced abilities @xcite related to factuality under adversarial queries @xcite or complex instructions following @xcite , as they are not necessary for other tasks and can lead to forgetting of the acquired abilities @xcite . in this paper, we address the above issues from the perspective of knowledge utilization and propose pkue (precise knowledge utilization enhancement) to enhance this capability with the task of precise qa. since knowledge utilization is a crucial factor for factuality @xcite and a fundamental ability of llm @xcite , its enhancement is expected to bring generalized improvement beyond hallucination mitigation. we take precise fact-seeking qa as a representative task, which is short-form with standard answers and simple without other attributes besides the correctness. these features make it a proper task to reflect factual hallucination as well as knowledge utilization @xcite @xcite aside from other abilities and prevent the mentioned issues of imprecise open-ended qa. to better enhance utilization rather than inject external information, we leverage the model's existing knowledge and conduct direct preference optimization (dpo) @xcite training on sampled data from the model itself, which provides more granular bi-directional controls and better generalization @xcite than uni-directional supervised fine-tuning (sft). this self-aligned approach preserves the model's distribution and limits the post-training shift, avoiding undesirable behaviors @xcite and additional hallucinations introduced by training on external new knowledge @xcite @xcite . however, a precise factual qa dataset with large scale and diverse domains is lacking for training. existing ones @xcite @xcite are usually outdated and fall short in fine-grained domain annotations, limiting their accuracy and diversity. to this end, we build factualbench, a large-scale dataset with 181k chinese qa data spanning 21 domains foot_0 . chinese is selected since it is a widely used language with a large community but still lacks high-quality datasets. we extract knowledge from the internet encyclopedia, a widely used pretraining corpus @xcite and can be taken as a knowledge base that llm has already seen. multiple filtering strategies are adopted to ensure data quality. evaluations on fac-tualbench reveal that while the task is not easy for llms, sampling with a higher temperature can yield more correct answers, which leaves space for better utilization of existing knowledge through self-alignment. extensive experiments on qwen2 @xcite and baichuan @xcite show that only pkue achieves consistent improvement on seven benchmarks covering factuality, helpfulness, and general skills in different forms and languages, presenting the best generalization. notably, pkue obtains 4× and 9× average improvement compared to existing methods @xcite @xcite . more ablation studies confirm the choices of self-generated data and dpo training. our work proves that improving the knowledge utilization on solely precise and simple qa can promote generalized enhancement, spanning from generative tasks to diverse forms of factual tasks, from factual tasks to other general tasks, and demonstrating cross-lingual transferability from chinese to english tasks. precise factual qa tasks include discriminative, multiple-choice, and generative forms. the former two @xcite @xcite have a limited answer space that allows models to guess the correct answer by chance, and therefore are unable to accurately judge whether the corresponding knowledge is possessed. generative datasets designed with adversarial intents @xcite can effectively provoke hallucinations but tend to focus on specific scenarios, limiting their capacities to reflect performance on more general questions. while large simple generative qa datasets @xcite @xcite ) exist, they are mostly built years ago with no domain annotations. in contrast, our annotated dataset offers a comprehensive and up-to-date assessment. ## method to mitigate factual hallucinations and prevent tradeoffs in other abilities beyond factuality, we propose pkue to augment the model's utilization of its existing knowledge. for training and evaluation, we build factualbench consisting of precise and simple qa data without malicious or misleading adversarial intents. in this section, we will introduce the dataset and the alignment method in detail. ## experiments in this section, we present the training results using pkue. comparison with the other three baselines validates our effectiveness, and more ablation studies are conducted to investigate how our detailed settings influence training outcomes. ## conclusion we propose pkue to mitigate factual hallucinations and achieve generalized improvement. precise and simple factual qa is selected as our training task, and we align models on self-generated preference data to enhance the model