Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation
<|reference_start|>Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation: Instruction-tuned Large Language Models (LLMs) have achieved remarkable performance across various benchmark tasks. While providing instructions to LLMs for guiding their generations is user-friendly, assessing their instruction-following capabilities is still unclarified due to a lack of evaluation metrics. In this paper, we focus on evaluating the instruction-following ability of LLMs in the context of story-ending generation, which requires diverse and context-specific instructions. We propose an automatic evaluation pipeline that utilizes a machine reading comprehension (MRC) model to determine whether the generated story-ending reflects instruction. Our findings demonstrate that our proposed metric aligns with human evaluation. Furthermore, our experiments confirm that recent open-source LLMs can achieve instruction-following performance close to GPT-3.5, as assessed through automatic evaluation.<|reference_end|>
@article{hida2024evaluation,
    title={Evaluation of Instruction-Following Ability for Large Language Models on
  Story-Ending Generation},
    author={Rem Hida, Junki Ohmura, Toshiyuki Sekiya},
    journal={arXiv preprint arXiv:2406.16356},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16356},
    primaryClass={cs.CL}
}