Tootfinder

"Global heating and urbanisation to blame for severity of UAE floods, study finds"
#UAE #UnitedArabEmirates #Climate

Attributing Responsibility in AI-Induced Incidents: A Computational Reflective Equilibrium Framework for Accountability
Yunfei Ge, Quanyan Zhu
https://arxiv.org/abs/2404.16957

Attributing Responsibility in AI-Induced Incidents: A Computational Reflective Equilibrium Framework for Accountability
The pervasive integration of Artificial Intelligence (AI) has introduced complex challenges in the responsibility and accountability in the event of incidents involving AI-enabled systems. The interconnectivity of these systems, ethical concerns of AI-induced incidents, coupled with uncertainties in AI technology and the absence of corresponding regulations, have made traditional responsibility attribution challenging. To this end, this work proposes a Computational Reflective Equilibrium (CRE)…

Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning
Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Daojian Zeng, Kang Liu, Jun Zhao
https://arxiv.org/abs/2402.18344

Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning
Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT). However, we find these CoT-like methods lead to a considerable number of originally correct answers turning wrong, which we define as the Toxic CoT problem. To interpret and mitigate this problem, we first utilize attribution tracing and causal tracing methods to probe the internal working mechanism of the LLM during CoT reasoning. Through comparisons, we pr…

APT-MMF: An advanced persistent threat actor attribution method based on multimodal and multilevel feature fusion
Nan Xiao, Bo Lang, Ting Wang, Yikai Chen
https://arxiv.org/abs/2402.12743

APT-MMF: An advanced persistent threat actor attribution method based on multimodal and multilevel feature fusion
Threat actor attribution is a crucial defense strategy for combating advanced persistent threats (APTs). Cyber threat intelligence (CTI), which involves analyzing multisource heterogeneous data from APTs, plays an important role in APT actor attribution. The current attribution methods extract features from different CTI perspectives and employ machine learning models to classify CTI reports according to their threat actors. However, these methods usually extract only one kind of feature and ig…

RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models
Maria Heuss, Maarten de Rijke, Avishek Anand
https://arxiv.org/abs/2403.16085 h…

RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models
Feature attributions are a commonly used explanation type, when we want to posthoc explain the prediction of a trained model. Yet, they are not very well explored in IR. Importantly, feature attribution has rarely been rigorously defined, beyond attributing the most important feature the highest value. What it means for a feature to be more important than others is often left vague. Consequently, most approaches focus on just selecting the most important features and under utilize or even ignor…

Aha. Fake-Account mit meinem Namen und Videoausschnitten auf #TikTok? Bin ich tatsächlich inzwischen so relevant? XD
(Und nein, natürlich ohne Attribution)

Joke's on the poster's designer. It's my life's philosophy to run everything on this Fold or Iron schedule. 😎
(Picture of a poster at a Goodwill store. The poster included no attribution.)
#poster #humor #found

Weekend #Plankton #Factoid 🦐🦠
An important grouping of marine #zooplankton is "temporary" #Meroplankton

CPR: Retrieval Augmented Generation for Copyright Protection
Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto
https://arxiv.org/abs/2403.18920

CPR: Retrieval Augmented Generation for Copyright Protection
Retrieval Augmented Generation (RAG) is emerging as a flexible and robust technique to adapt models to private users data without training, to handle credit attribution, and to allow efficient machine unlearning at scale. However, RAG techniques for image generation may lead to parts of the retrieved samples being copied in the model's output. To reduce risks of leaking private information contained in the retrieved set, we introduce Copy-Protected generation with Retrieval (CPR), a new method …

Whose LLM is it Anyway? Linguistic Comparison and LLM Attribution for GPT-3.5, GPT-4 and Bard
Ariel Rosenfeld, Teddy Lazebnik
https://arxiv.org/abs/2402.14533

Whose LLM is it Anyway? Linguistic Comparison and LLM Attribution for GPT-3.5, GPT-4 and Bard
Large Language Models (LLMs) are capable of generating text that is similar to or surpasses human quality. However, it is unclear whether LLMs tend to exhibit distinctive linguistic styles akin to how human authors do. Through a comprehensive linguistic analysis, we compare the vocabulary, Part-Of-Speech (POS) distribution, dependency distribution, and sentiment of texts generated by three of the most popular LLMS today (GPT-3.5, GPT-4, and Bard) to diverse inputs. The results point to signific…