Low-Hallucination and Efficient Coreference Resolution with LLM s (2025)

Abstract: Large Language Models (LLMs) have shown promising results in coreference resolution, especially after fine-tuning. However, recent generative approaches face a critical issue: hallucinations—where the model generates content not present in the original input. These hallucinations make evaluation difficult and decrease overall performance. To address this issue, we analyze the underlying causes of hallucinations and propose a low-hallucination and efficient solution. Specifically, we introduce Efficient Constrained Decoding for Coreference Resolution, which maintains strong robustness while significantly improving computational efficiency. On the English OntoNotes development set, our approach achieved slightly better performance than previous state-of-the-art methods, while requiring substantially fewer parameters.

## introduction coreference resolution (cr) is the task of detecting and grouping mentions that refer to the same entity in text. this task requires deep linguistic and contextual understanding, which makes it a vital component for many downstream nlp applications, including information extraction, text summarization, chatbots, and dialogue systems. as a result, cr has attracted significant attention from the nlp community @xcite . most recently, the rise of large language models (llms) has transformed nlp by providing strong contextual understanding and knowledge integration across tasks. recent studies have demonstrated llms' effectiveness at coreference resolution, especially in zero-and few-shot scenarios @xcite @xcite @xcite . @xcite achieved superior performance template input: constrained decoding with llm [john] (#) and [lily](#) go to the [market](#). @xmath0... repeat [john] (# lily and (# is has also ... cluster_1 ... @xmath1... @xmath2... @xmath3... ... ... which ... are during template text unconstrained text forbidden token allowed token legend: cluster_1) and [lily] (# go to the [market] (# cluster_3) [john] (# cluster_2) llm prompt input: annotate all entity mentions in the following text with coreference clusters. input: [john] (#) and [lily](#) go to the [market](#). ## related work our work primarily builds on two studies by @xcite and @xcite . in addition, we draw inspiration from the use of constrained decoding in named entity recognition @xcite , and adapt this approach to the task of coreference resolution. since both document-annotationbased coreference resolution and constrained decoding are central to our method, we discuss them in this section. other related work is discussed in appendix a. ## hallucination analysis and causal hypotheses we argue that one of the primary causes of hallucinations in coreference resolution is llms' inability to strictly follow the original content in the prompt, particularly with long and structurally complex input text. this complexity often stems from missing punctuation, ambiguous pronouns, or omitted contextual information (e.g., dialogues lacking background) -factors that significantly increase the risk of hallucination during generation. one possible underlying reason is attention dilution in transformer-based models @xcite . as input length grows, the attention weight for each token in the softmax distribution decreases. this dilution makes it harder for the model to focus on crucial input elements, causing it to rely more heavily on pre-trained language patterns and potentially deviate from the source text. moreover, @xcite have demonstrated that llms tend to focus disproportionately on the beginning and end of long inputs while neglecting important middle content, further increasing the hallucination risk. based on the above analysis, we believe that preventing hallucinations in coreference resolution requires constraining the model from generating incorrect tokens when processing long and complex input texts. ## hypothesis-driven methods to test our hypothesis, we use two methods for preventing incorrect token generation: iterative document generation (our previous work) and constrained decoding for coreference resolution. ## conclusion hallucinations are a key challenge for llm-based coreference resolution, arising from models' inabil-ity to faithfully follow the input text. to verify this hypothesis, we revisited iterative document generation from our previous work, which demonstrated the benefit of constraining the output space. building on this insight, this paper introduces efficient constrained decoding for coreference resolution, which not only eliminated hallucinations but also achieved substantially better efficiency, surpassing previous state-of-the-art results on ontonotes while using fewer parameters.