Mitigating Knowledge Conflicts in Data-to-Text Generation via the Internalization of Fact Extraction

Large Language Models (LLMs) have made remarkable advancements in Natural Language Generation. Nonetheless, LLMs are prone to encountering knowledge conflicts, scenarios where the generated statement exhibits either twisted facts that contradict the source or unverified facts that lack substantiation from the source. We posit that LLMs should possess the capability to differentiate between facts grounded in evidence and those derived from inference. A promising avenue for mitigating knowledge conflicts involves prompting LLMs to externalize the multi-step reasoning during text generation within the chain-of-thought paradigm. Nevertheless, such externalization of reasoning has only shown benefits for sufficiently large LLMs (e.g., those exceeding 100B parameters). Despite attempts to distill this ability into smaller LLMs, the reliance on large and inefficient teacher models remains a necessity. Thus, we introduce the Internalization of Fact Extraction (IFE), which optimizes instruction-tuning, empowering smaller LLMs to internalize the fact extraction process during text generation within the auxiliary learning paradigm. Consequently, models can directly generate statements where knowledge conflicts are mitigated. Specifically, we design an auxiliary fact extraction task built upon the primary text generation task, requiring models to identify evidential facts and filter out inferential facts after text generation. The auxiliary training dataset for the auxiliary fact extraction task was constructed using word-stemming and string-matching techniques. By leveraging auxiliary learning, the models’ ability to internalize the fact extraction process emerges, thereby enhancing its performance in the primary text generation task. Extensive experiments and analyses are conducted on three benchmark data-to-text generation datasets (i.e., FeTaQA, DialogSum, and SAMSum), covering sub-tasks including table-based text generation and dialogue-based text generation. Experimental results show that our IFE instruction-tuned models outperform the vanilla instruction-tuned baselines in terms of both factuality and quality without introducing any inference latency, demonstrating the superiority of IFE instruction-tuning in mitigating knowledge conflicts.