G en D ecider: Integrating “None of the Candidates” Judgments in Zero-Shot Entity Linking Re-ranking (2024)

Abstract: We introduce GenDecider, a novel re-ranking approach for Zero-Shot Entity Linking (ZSEL), built on the Llama model. It innovatively detects scenarios where the correct entity is not among the retrieved candidates, a common oversight in existing re-ranking methods. By autoregressively generating outputs based on the context of the entity mention and the candidate entities, GenDecider significantly enhances disambiguation, improving the accuracy and reliability of ZSEL systems, as demonstrated on the benchmark ZESHEL dataset. Our code is available at https://github.com/kangISU/GenDecider.

## introduction zero-shot entity linking (zsel) @xcite , a crucial task in nlp, links entity mentions @xcite ) in text to corresponding entities in a knowledge base (kb), when no labeled examples of those entities are available. the importance of this task stems from its ability to handle entities dynamically, particularly in evolving kbs where new entities frequently emerge. the prevailing approach in zsel, exemplified by the blink method @xcite , adopts a two-step process: initial retrieval of candidate entities followed by a re-ranking phase. while extensive research has improved the retrieval stage @xcite @xcite @xcite , the reranking phase, which is critical for final decisionmaking, has received comparatively less attention. moreover, a significant oversight in existing reranking studies @xcite @xcite is the assumption that the correct entity is always among the retrieved candidates. this assumption, however, often does not hold in zero-shot settings, leading to the prevalence of what we call "none of the candidates" (noc for short) cases. when the correct entity is not among the retrieved candidates, opting for a noc prediction is more beneficial than forcibly making a false positive prediction in real applications. having noc predictions can also offer feedback to the retrieval phase by highlighting the limitations of retrievers in zero-shot settings. this paper introduces gendecider, a novel approach that integrates noc judgments into the zsel re-ranking process. gendecider formulates the re-ranking task as a generative process using the recent llama model @xcite . given the context of an entity mention and the retrieved candidates, gendecider autoregressively generates an output that is either the id of the correct entity candidate or a noc judgment. this approach allows for direct interactions between the mention context and the candidates within the same input, facilitating more accurate disambiguation. moreover, by supporting noc judgments, gendecider enhances the reliability of zsel systems. the contributions of this work are twofold. firstly, it presents a novel re-ranking formulation that addresses a significant gap in existing research by effectively detecting noc scenarios. secondly, the proposed method demonstrates a comprehensive approach to disambiguation, improving both the accuracy and applicability of zsel systems. ## related work entity linking (el) methods can be broadly classified into generation-based and retrievalbased. generation-based methods, such as genre @xcite , directly generate entity titles but struggle with new entities in zero-shot settings @xcite . in contrast, retrieval-based methods, more suitable for zero-shot settings, follow a two-step approach: candidate retrieval and reranking. therefore, we follow the line of retrievalbased approaches for zero-shot settings and particularly enhance the re-ranking step. ## conclusion this paper presents gendecider, an innovative reranking approach for zsel that adeptly incorporates noc judgments. our extensive experiments on zeshel demonstrate that gendecider achieves superior performance in challenging disambiguation scenarios. the results underscore the importance of noc consideration in improving the reliability in the re-ranking phase.