arXiv cs.CL· Indu Panigrahi, Tal August·· 3 天前AI 评分23
让 LLM 按需调整文本复杂度：五款模型在 557 条科学查询上的表现评估
Explain Like I'm 5 or Whatever I Choose: Evaluating the Potential for Interactive Complexity in Language Model Responses
AI 导读
一项用户研究评估了五款不同家族、规模和推理能力的 LLM 能否沿复杂度连续体生成多个科学信息摘要，以支持用户直接操控文本。在 557 条科学查询上，模型虽能改变回答复杂度，但多数变化不一致，复杂度指标朝正确方向移动的比例最高仅 33.67%。
正文 · 原文
Abstract:Given the increased use of large language models (LLMs) for reading and seeking information and the varying needs and backgrounds of users, direct manipulation of text can be a valuable mechanism for end-user control. However, enabling direct manipulation requires that models generate text consistent with user-requested changes. We focus on one such setting, manipulating the complexity of scientific information, and ask if models can generate multiple summaries along a continuum of text complexity that enable effective user selection and control. Through a within-subjects user study, we identify reliable complexity measures and a criterion for how models should conform to these measures. We evaluate five LLMs of different families, sizes, and reasoning abilities on $557$ scientific queries. We find that while models can vary complexity across responses, most changes remain inconsistent, with models shifting the complexity measures in the correct direction at most $33.67$% of the time.
| Comments: | Preprint |
| Subjects: | Computation and Language (cs.CL); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2606.06788 [cs.CL] |
| (or arXiv:2606.06788v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06788 arXiv-issued DOI via DataCite |
Submission history
From: Indu Panigrahi [view email]
[v1]
Fri, 5 Jun 2026 00:14:44 UTC (4,050 KB)
[v2]
Wed, 14 Oct 2026 13:55:08 UTC (4,071 KB)
来源：arXiv cs.CL · arxiv.org