Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for Fine-grained Text Evaluations
<|reference_start|>Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for Fine-grained Text Evaluations: Large Language Models (LLMs) show promise as a writing aid for professionals performing legal analyses. However, LLMs can often hallucinate in this setting, in ways difficult to recognize by non-professionals and existing text evaluation metrics. In this work, we pose the question: when can machine-generated legal analysis be evaluated as acceptable? We introduce the neutral notion of gaps, as opposed to hallucinations in a strict erroneous sense, to refer to the difference between human-written and machine-generated legal analysis. Gaps do not always equate to invalid generation. Working with legal experts, we consider the CLERC generation task proposed in Hou et al. (2024b), leading to a taxonomy, a fine-grained detector for predicting gap categories, and an annotated dataset for automatic evaluation. Our best detector achieves 67% F1 score and 80% precision on the test set. Employing this detector as an automated metric on legal analysis generated by SOTA LLMs, we find around 80% contain hallucinations of different kinds.<|reference_end|>
@article{hou2024gaps,
    title={Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for
  Fine-grained Text Evaluations},
    author={Abe Bohan Hou, William Jurayj, Nils Holzenberger, Andrew Blair-Stanek,
  Benjamin Van Durme},
    journal={arXiv preprint arXiv:2409.09947},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09947},
    primaryClass={cs.CL cs.CY}
}