SCOPE is a training‑free generative prompt‑compression framework that reduces LLM input length by chunking a prompt into semantically coherent segments, rewriting each chunk to be more concise, and then reconstructing a coherent prompt. Unlike token‑removal methods, SCOPE’s chunk‑level rewriting preserves critical information and text coherence, and includes optimization techniques for finer‑grained control of compression ratios. Extensive evaluations on question‑answering and summarization tasks show that SCOPE consistently outperforms selective compression baselines, especially at high compression ratios.
By Tinghui Zhang, Yifan Wang, Daisy Zhe Wang
arXiv:2511.23271v2 Announce Type: replace
Abstract: Long system prompts are widely used to steer Large Language Models (LLMs), but repeatedly processing them at inference time is inefficient and cons...
By Jiancheng Dong, Pengyue Jia, Jingyu Peng, Maolin Wang, Yuhao Wang, Lixin Su, Xin Sun, Shuaiqiang Wang, Dawei Yin, Xiangyu Zhao
arXiv:2609.13154v1 Announce Type: new
Abstract: Recent advances in large language models (LLMs) have made prompts increasingly large and complex. Techniques such as chain-of-thought reasoning (Wei et...
By Shamin Chokshi
The paper introduces soft‑prompt tuning as a lightweight, automated method for evaluating large language models (LLMs). By optimizing only ten soft‑prompt vectors—about 0.0006% of a 7B model’s parameters—within 150 steps, the approach adapts models to specific benchmark formats, improving format compliance while preserving knowledge accuracy. Experiments across seven models and eleven datasets show that this method matches strong evaluation‑harness baselines, enhances evaluation signals on challenging datasets, and yields cleaner outputs that align better with exact‑match scoring and independent LLM judges.
By Selen Erkan, Bastian Boll, Kristian Kersting, Bj\"orn Deiseroth, Letitia Parcalabescu
The paper introduces Prompt2Box, a method that embeds prompts into a box embedding space to capture both semantic similarity and specificity relations, addressing the limitation of traditional vector embeddings that conflate topical similarity with specificity. Using a trained encoder on existing and synthesized datasets, Prompt2Box achieves significant improvements, reducing specificity prediction error by 45% over a prompt-length baseline and identifying 13.5% more LLM weaknesses in hierarchical clustering compared to vector baselines. The authors also present a novel dimension‑reduction technique for visualizing and comparing box embeddings, and provide the code on GitHub.
By Neeladri Bhuiya, Shib Sankar Dasgupta, Andrew McCallum, Haw-Shiuan Chang
The behavior of contemporary generative Large Language Models (LLMs) is directly shaped by prompts, unstructured texts that describe the desired output and model behavior. In this paper we argue that prompts are linguistic objects that merit investigation in their own right.
arXiv:2608.21074v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly used in research workflows and software development pipelines, yet their output remains sensitive to in...
By Erik Thureck, Robert K\"uhnen, Tim Jacobowitz
arXiv:2601. 22888v4 Announce Type: replace-cross Abstract: More than 80% of the 1.
By Jio Oh, Paul Vicinanza, Thomas Butler, Steven Euijong Whang, Dezhi Hong, Amani Namboori
arXiv:2511. 19829v3 Announce Type: replace Abstract: Prompt optimization has become a central mechanism for eliciting strong performance from LLMs, and recent work has made substantial progress by proposing diverse prompt evaluation metrics and optimization strategies.
By Ke Chen, Yifeng Wang, Hassan Almosapeeh, Haohan Wang
arXiv:2607. 25335v1 Announce Type: cross Abstract: Prompt compression shortens LLM input to reduce inference cost, yet existing methods score token importance through LM forward passes.
By Jianfei Ma, Zhaoxin Feng, Emmanuele Chersoni, Si Chen
arXiv:2607. 08399v1 Announce Type: cross Abstract: Large language models process prompts by propagating activations through dozens of layers before generating a response.
By Thibaud Ardoin, Semira Einsele, Evis Bregu, Gerhard Wunder
arXiv:2608. 10939v1 Announce Type: cross Abstract: Multilingual short-text classification supports operational systems such as content moderation, customer support routing, and intent recognition, yet aggregate evaluation often hides large differences between high-resource and low-resource languages.
By Wajdi Ben Saad, Safa Madiouni