AELC : Adaptive Entity Linking with LLM -Driven Contextualization (2025)

Abstract: Entity linking (EL) focuses on accurately associating ambiguous mentions in text with corresponding entities in a knowledge graph. Traditional methods mainly rely on fine-tuning or training on specific datasets. However, they suffer from insufficient semantic comprehension, high training costs, and poor scalability. Large Language Models (LLMs) offer promising solutions for EL, but face key challenges: weak simple-prompt performance, costly fine-tuning, and limited recall and precision due to the lack of LLMs use in candidate generation. Building on this, we introduce a novel framework: **A**daptive **E**ntity **L**inking with LLM-Driven **C**ontextualization. AELC, for the first time, introduces the combination of high-density key information condensation prompt and tool-invocation strategy, using a unified format semantic filtering strategy and an adaptive iterative retrieval mechanism to dynamically optimize the candidate set, significantly enhancing both precision and coverage. Furthermore, we innovatively reformulate the EL task as a multiple-choice problem, enabling multi-round reasoning to substantially improve the model’s discriminative capability and robustness. Experiments on four public benchmark datasets demonstrate that AELC achieves state-of-the-art performance. Further ablation studies validate the effectiveness of each module.

## introduction entity linking plays a vital role in various nlp downstream tasks, including reading comprehension @xcite and intelligent question answering @xcite . el typically involves two stages: candidate generation (retrieving potential entities) and candidate re-ranking (selecting the most suitable entity from the candidate set). effective el improves information retrieval and * corresponding author. xiaoying bai, minghao hu improves the accuracy and personalization of conversational systems. however, its performance is often constrained by the quality of candidates. traditional el methods typically rely on small pre-trained models for candidate generation @xcite . while efficient, these models often lack deep semantic understanding, leading to candidate sets with low recall and precision. when the correct entity is missing from the candidate set, existing methods struggle to cope. some approaches @xcite overlook this issue during evaluation, while others introduce a 'none' class to bypass it. however, such strategies are unrealistic in practical scenarios, limiting the robustness and applicability of el in complex settings. in recent years, the rapid advancement of llms has introduced new opportunities for el, owing to their powerful semantic modeling and reasoning capabilities acquired through training on large-scale datasets. several studies have explored the integration of llms into el. for example, summc @xcite reformulates the el task as a multiplechoice problem by generating mention summaries to aid entity selection; chatel @xcite proposes a structured three-stage framework that systematically guides llms to produce more accurate linking outputs; and llmael @xcite enhances el by enriching input with llmgenerated mention-focused descriptions, while retaining traditional models for task-specific processing. despite their potential, these approaches still face significant challenges: most rely on simple prompts, which fail to fully exploit the reasoning capabilities of llms; fine-tuning or training llms remains computationally expensive and impractical in many scenarios; and critically, the candidate generation stage often fails to leverage llms, resulting in suboptimal recall and precision, thereby limiting overall el performance. to overcome these limitations, we introduce aelc (adaptive entity linking with llm-driven contextualization), a novel framework designed to fully leverage the capabilities of llms for robust el. the aelc framework consists of three components: llm-driven key information condensation (llm-kic), adaptive semantic fusion for dynamic candidate generation (asf-dcg), and llm-powered task formalization transformation (llm-tft). llm-kic refines the document-level entity linking task into paragraph-level subtasks, aligning better with llm input constraints. it employs a key information condensation prompt with a built-in chain-of-thought structure to distill the context of a mention into high-density key information. asf-dcg dynamically generates candidate entities based on an online knowledge graph through a combination of tool invocation, semantic filtering, and adaptive iterative strategies. finally, llm-tft enhances the model's adaptability to the el task by reformulating it into a multiple-choice format and leveraging in-context learning, thereby significantly improving overall performance. to comprehensively assess the effectiveness of aelc, we conducted experiments on four widely-used el benchmark datasets, comparing our approach against both traditional el models and recent llm-based methods. results show that aelc consistently outperforms baselines in linking accuracy. these findings highlight the strength of our framework in enhancing el performance through the effective integration of external tools and the construction of diverse task-specific prompts. to summarize, the main contributions of this work are as follows: ## preliminary let d be a single document from the document collection d. building upon the foundation laid by previous work in entity linking, we assume that the relevant information pertaining to target mentions in document d has been obtained through a named entity recognizer. thus, let m @xmath0, m 2 ,...,m n } represent the set of n target mentions contained within document d. let e be the set of all entities in the dynamic online knowledge graph g. let c be the candidate set for the target mention, where c ⊆ e. the input ψ for unsupervised entity linking comprises a document d containing target mentions m d and an online source knowledge graph g. the goal of this task is to find an equivalent candidate c j for the target mention m i without pre-training, where candidate c j is drawn from g: ## approach in this section, we introduce aelc with the aim of improving the performance of conventional unsupervised el methods. the overview of the framework is shown in figure 1 . ## conclusion in this work, we introduce a novel framework,adaptive entity linking with llm-driven contextualization (aelc). by leveraging highdensity key information condensation prompt and tool invocation strategy, aelc extracts crucial information for target mentions. moreover, the candidate filtering strategy combined with the candidate adaptive iterative strategy improves the quality of the candidate set. furthermore, we reformulate the el task as a multiple-choice problem, enhancing the adaptability and accuracy of llms in performing language tasks. experimental results demonstrate that aelc achieves state-of-the-art performance on four benchmark datasets. in future work, we will investigate more effective and efficient ways to combine llms and tools for entity linking, e.g.,auto prompt learning, and extend this framework to multimodal entity linking task.