The paper investigates how small lexical changes in prompts can cause large performance swings in large language models. Using a dataset of 132,000 prompt variants, the authors uncover a scaling law linking higher average task performance to lower variance and greater robustness. They identify domain-specific terminology and explicit action directives as key linguistic factors that stabilize prompts, and propose an automated Prompt-Refining Agent that reduces performance variance by 40.7% in code generation while maintaining or improving mean performance.
By Qipeng Xie, Zi Liang, Jiafei Wu, Yufei Chen, Weizheng Wang, Wenao Ma, Zhong Ming, Haiqin Yang, Kaishun Wu
arXiv:2606. 17164v1 Announce Type: cross Abstract: Prompting has become the primary interface between humans and generative AI, yet many natural language prompts remain fragile: roles, goals, constraints, and expected outputs are often buried in prose or left implicit.
By Enkhzol Dovdon
The paper presents a survey of 129 public large language model (LLM) prompt datasets, totaling over 1.22 TB and 673 million instances, and introduces a unified taxonomy for them. By analyzing seven datasets in depth, the authors identify lexical, syntactic, and semantic patterns that differentiate prompts from general text, and evaluate these patterns for tasks such as prompt filtering, source domain routing, and response quality assessment. They demonstrate that a 63‑dimensional linguistic feature set extracted on a CPU can match over 91 % of the F1 score of GPU‑based sentence embeddings while halving latency, and that structural features can effectively route prompts across datasets, though they may negatively impact response quality when prompt length is controlled.
By Yuanming Zhang, Yan Lin, Arijit Khan, Huaiyu Wan
arXiv:2507.21831v2 Announce Type: replace-cross
Abstract: LLMs are seeing widespread use for task automation, including automated coding in the social sciences. However, even though researchers have...
By Andreas Reich, Claudia Thoms, Tobias Schrimpf
HoarePrompt is a new method that applies program verification concepts to natural language requirements, using large language models to generate step‑by‑step natural language descriptions of program states. It incorporates a few‑shot k‑induction technique to handle loops and then evaluates whether the annotated program satisfies the requirements. On the CoCoClaNeL dataset, HoarePrompt raises the Matthews correlation coefficient by 61% over zero‑shot chain‑of‑thought prompts and by 106% over test‑generation classifiers, with the inductive reasoning component adding a 26% MCC improvement.
By Dimitrios Stamatios Bouras, Yihan Dai, Tairan Wang, Yingfei Xiong, Sergey Mechtaev
arXiv:2602. 03045v2 Announce Type: replace Abstract: Large language models have recently enabled text-to-CAD systems that synthesize parametric CAD programs (e.
By Bo Yuan, Zelin Zhao, Petr Molodyk, Bin Hu, Yongxin Chen
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming conventions remains largely unexplored.
arXiv:2608.27831v1 Announce Type: cross
Abstract: Coding agents are now commonly evaluated on the SWE-bench family of benchmarks, whose tasks are built from curated GitHub issues--long, structured, a...
By Gyuhyeong Kim, Hyojung Gwon, Jeonghyeon Kim, Kyuhong Shim, Sunjae Lee
The paper investigates prompt minimization, aiming to reduce prompts to their smallest, most information-dense form without losing output fidelity. It argues that shorter prompts lower computational overhead and inference latency, especially when large contexts are unnecessarily included, and that longer prompts can harm LLM reasoning and accuracy. The authors propose three frameworks to identify minimal prompts and show that these often produce outputs comparable to longer versions, highlighting redundancy in the input space and opening new avenues for efficient prompt engineering.
By Marius F. R. Juston, Kevin A. Karim, Jonathan Gao, Kevin C. Li, Rudhi Bashambu
arXiv:2607. 14816v1 Announce Type: cross Abstract: Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias.
By Saima Afrin, Alessandro Midolo, Camilo Escobar-Vel\'asquez, Mario Linares-V\'asquez, Weiyuan Ding, Bowen Xu, Massimiliano Di Penta, Antonio Mastropaolo
arXiv:2608. 11513v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software engineering workflows, helping developers write, debug, test, and maintain code.
By Alex Deaconu, Anubhav Gupta, Manaal Basha, Nicholas Haydu, Gema Rodr\'iguez-P\'erez
The paper introduces a fine-grained method called interactions to analyze prompt sensitivity in large language models (LLMs). By decomposing output scores into nonlinear interactions, the authors show that subtle prompt changes can destabilize these interactions even when overall outputs stay unchanged. They propose an Interaction-based Prompt Sensitivity (IPS) metric and use it to evaluate 50 open-source LLMs, finding that supervised fine‑tuning, larger model scales, dense architectures, and few‑shot learning all reduce prompt sensitivity, primarily by stabilizing low‑order interactions.
By Ruiyang Qin, Qingzhuo Wang, Tian Wang, Zhihua Wei, Wen Shen