O ne N et: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting (2024)

Abstract: Entity Linking (EL) is the process of associating ambiguous textual mentions to specific entities in a knowledge base.Traditional EL methods heavily rely on large datasets to enhance their performance, a dependency that becomes problematic in the context of few-shot entity linking, where only a limited number of examples are available for training. To address this challenge, we present OneNet, an innovative framework that utilizes the few-shot learning capabilities of Large Language Models (LLMs) without the need for fine-tuning. To the best of our knowledge, this marks a pioneering approach to applying LLMs to few-shot entity linking tasks. OneNet is structured around three key components prompted by LLMs: (1) an entity reduction processor that simplifies inputs by summarizing and filtering out irrelevant entities, (2) a dual-perspective entity linker that combines contextual cues and prior knowledge for precise entity linking, and (3) an entity consensus judger that employs a unique consistency algorithm to alleviate the hallucination in the entity linking reasoning.Comprehensive evaluations across seven benchmark datasets reveal that OneNet outperforms current state-of-the-art entity linking methods.

## introduction entity linking (el), also known as named entity disambiguation (ned), entails the process of linking ambiguous textual mentions to specific entities in a knowledge base, as shown in figure 1 @xcite . this process is a critical element of both natural language processing (nlp) and information retrieval (ir) @xcite . to enhance the accuracy of el, researchers employ two primary methods: discriminative models and generative models. discriminative models represent mentions and entities through embeddings and link entities by calculating the similarity. to ## conclusion in this study, we introduced onenet, a novel framework for few-shot entity linking by leveraging large language model prompts without fine-tuning. specifically, onenet was comprised of three key llm-prompted components: the entity reduction processor, which was designed for efficient text condensation by summarizing entity descriptions and irrelevant entity filtering; the dual-perspective entity linker, which considered both contextual information and prior knowledge to provide a balanced analysis; and the entity consensus judger, which was instrumental in reducing hallucinations through a consistency merger algorithm. our framework demonstrated superior performance on seven datasets. our future research will aim to merge mention detection within our model.