Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities. Yet the generative nature still makes the generated content suffer from hallucinations, thus unsuitable for entity-centric tasks like entity linking (EL) requiring precise entity predictions over a large knowledge base. We present Instructed Generative Entity Linker (INSGENEL), the first approach that enables casual language models to perform entity linking over knowledge bases. Several methods to equip language models with EL capability were proposed in this work, including (i) a sequence-to-sequence training EL objective with instruction-tuning, (ii) a novel generative EL framework based on a light-weight potential mention retriever that frees the model from heavy and non-parallelizable decoding, achieving 4$\times$ speedup without compromise on linking metrics. INSGENEL outperforms previous generative alternatives with +6.8 F1 points gain on average, also with a huge advantage in training data efficiency and training compute consumption. In addition, our skillfully engineered in-context learning (ICL) framework for EL still lags behind INSGENEL significantly, reaffirming that the EL task remains a persistent hurdle for general LLMs.

翻译：基于大语言模型（LLMs）的生成式方法在需要复杂推理能力的任务中展现出涌现能力。然而，生成式本质仍会导致生成内容出现幻觉，因此不适用于实体链接（EL）等需要在大规模知识库上进行精确实体预测的实体导向型任务。我们提出指令型生成式实体链接器（INSGENEL），这是首个使因果语言模型能够对知识库执行实体链接的方法。本工作提出了多种赋予语言模型EL能力的方法，包括：(i) 结合指令微调的序列到序列训练EL目标；(ii) 基于轻量级潜在提及检索器的新型生成式EL框架，该框架使模型摆脱繁重且不可并行化的解码过程，在不影响链接指标的情况下实现4倍加速。INSGENEL在平均F1分数上以+6.8分的优势超越先前生成式替代方案，同时在训练数据效率和训练计算消耗方面具有显著优势。此外，我们精心设计的面向EL的上下文学习（ICL）框架仍明显落后于INSGENEL，这再次证实EL任务仍是通用大语言模型面临的持久障碍。