Abstract
Large language models (LLMs) are often supplemented with external knowledge to provide information not encoded in their parameters or to reduce hallucination. In such cases, we expect the model to generate responses by grounding its response in the provided external context. However, prior work has shown that simply appending context at inference time does not ensure grounded generation. To address this, we propose Context-INformed Grounding Supervision (CINGS), a post-training supervision in which the model is trained with relevant context prepended to the response, while computing the loss only over the response tokens and masking out the context. Our experiments demonstrate that models trained with CINGS exhibit stronger grounding in both textual and visual domains compared to standard instruction-tuned models. In the text domain, CINGS outperforms other training methods across 11 information-seeking datasets and is complementary to inference-time grounding techniques. In the vision-language domain, replacing a vision-language model's LLM backbone with a CINGS-trained model reduces hallucinations across four benchmarks and maintains factual consistency throughout the generated response. This improved grounding comes without degradation in general downstream performance. Finally, we analyze the mechanism underlying the enhanced grounding in CINGS and find that it induces a shift in the model's prior knowledge and behavior, implicitly encouraging greater reliance on the external context.
Abstract (translated)
大型语言模型（LLMs）通常会通过补充外部知识来提供其参数中未编码的信息，或者减少幻觉的产生。在这种情况下，我们期望模型在生成响应时能够以提供的外部上下文为基础。然而，先前的研究表明，仅在推理阶段附加上下文并不能确保基于上下文的生成。为了解决这个问题，我们提出了Context-INformed Grounding Supervision（CINGS），这是一种训练后的监督方法，在该方法中，模型使用与响应相关的上下文进行预处理，同时计算损失只针对响应标记，并屏蔽掉上下文部分。我们的实验表明，使用CINGS训练的模型在文本和视觉领域都表现出更强的基于上下文生成能力，优于标准指令调优模型的表现。在文本领域，CINGS在11个信息寻求数据集上超过了其他训练方法，在推理时的基于上下文生成技术中也表现出了互补性。在视觉语言领域，通过将一个视觉语言模型中的大型语言模型主干替换为使用CINGS训练的模型，其幻觉减少，并在整个生成响应过程中保持了事实一致性。这种改进的基于上下文生成并没有导致下游性能的整体下降。最后，我们分析了CINGS背后增强基于上下文生成机制的原因，发现它使模型的知识和行为产生了变化，隐式地鼓励更大程度上依赖于外部上下文。
URL
https://arxiv.org/abs/2506.15480
