Tootfinder

PLACE: Prompt Learning for Attributed Community Search
Shuheng Fang, Kangfei Zhao, Rener Zhang, Yu Rong, Jeffrey Xu Yu
https://arxiv.org/abs/2507.05311 htt…

PLACE: Prompt Learning for Attributed Community Search
In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enlightened by prompt-tuning in Natural Language Processing (NLP), where learnable prompt tokens are inserted to contextualize NLP queries, PLACE integrates structural and learnable prompt tokens into the graph as a query-dependent refinement mechanism, forming a prompt-augmented graph. Within this prompt-augmented graph structure, the learned prompt tokens s…

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques
Walid Mohamed Aly, Taysir Hassan A. Soliman, Amr Mohamed AbdelAziz
https://arxiv.org/abs/2507.05123

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques
Large Language Models (LLMs) continue to advance natural language processing with their ability to generate human-like text across a range of tasks. Despite the remarkable success of LLMs in Natural Language Processing (NLP), their performance in text summarization across various domains and datasets has not been comprehensively evaluated. At the same time, the ability to summarize text effectively without relying on extensive training data has become a crucial bottleneck. To address these issu…

Trojan Horse Hunt in Time Series Forecasting for Space Operations
Krzysztof Kotowski, Ramez Shendy, Jakub Nalepa, Przemys{\l}aw Biecek, Piotr Wilczy\'nski, Agata Kaczmarek, Dawid P{\l}udowski, Artur Janicki, Evridiki Ntagiou
https://arxiv.org/abs/2506.01849

Trojan Horse Hunt in Time Series Forecasting for Space Operations
This competition hosted on Kaggle (https://www.kaggle.com/competitions/trojan-horse-hunt-in-space) is the first part of a series of follow-up competitions and hackathons related to the "Assurance for Space Domain AI Applications" project funded by the European Space Agency (https://assurance-ai.space-codev.org/). The competition idea is based on one of the real-life AI security threats identified within the project -- the adversarial poisoning of continuously fine-tuned satellite telemetry fore…

Playing with Sollya today, which is a tool for generating math function polynomials. I've managed to build a gamma function for 32, 64, 80 and 128 bits which doesn't totally suck. Range reduction to 1-2, then an 11th order polynomial (for 32-bits).
https://github.…

picolibc/newlib/libm/sollya/gamma_inc.h at gamma-accuracy · picolibc/picolibc
picolibc - a C library designed for embedded 32- and 64- bit systems. - picolibc/picolibc

TopicAttack: An Indirect Prompt Injection Attack via Topic Transition
Yulin Chen, Haoran Li, Yuexin Li, Yue Liu, Yangqiu Song, Bryan Hooi
https://arxiv.org/abs/2507.13686

TopicAttack: An Indirect Prompt Injection Attack via Topic Transition
Large language models (LLMs) have shown remarkable performance across a range of NLP tasks. However, their strong instruction-following capabilities and inability to distinguish instructions from data content make them vulnerable to indirect prompt injection attacks. In such attacks, instructions with malicious purposes are injected into external data sources, such as web documents. When LLMs retrieve this injected data through tools, such as a search engine and execute the injected instruction…

On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey
Meishan Zhang, Xin Zhang, Xinping Zhao, Shouzheng Huang, Baotian Hu, Min Zhang
https://arxiv.org/abs/2507.20783

On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey
Text embeddings have attracted growing interest due to their effectiveness across a wide range of natural language processing (NLP) tasks, such as retrieval, classification, clustering, bitext mining, and summarization. With the emergence of pretrained language models (PLMs), general-purpose text embeddings (GPTE) have gained significant traction for their ability to produce rich, transferable representations. The general architecture of GPTE typically leverages PLMs to derive dense text repres…

A Unifying Scheme for Extractive Content Selection Tasks
Shmuel Amar, Ori Shapira, Aviv Slobodkin, Ido Dagan
https://arxiv.org/abs/2507.16922 https://

A Unifying Scheme for Extractive Content Selection Tasks
A broad range of NLP tasks involve selecting relevant text spans from given source texts. Despite this shared objective, such \textit{content selection} tasks have traditionally been studied in isolation, each with its own modeling approaches, datasets, and evaluation metrics. In this work, we propose \textit{instruction-guided content selection (IGCS)} as a beneficial unified framework for such settings, where the task definition and any instance-specific request are encapsulated as instructio…