Tootfinder

How some people are using the term "slop" as a descriptor for low-grade AI material, after emerging in reaction to the release of AI art generators in 2022 (Benjamin Hoffman/New York Times)
https://www.nytimes.com/2024/06/11…

Is Slop A.I.’s Answer to Spam? A Phrase Emerges for Bad Search.
A new term has emerged to describe dubious A.I.-generated material.

DefAn: Definitive Answer Dataset for LLMs Hallucination Evaluation
A B M Ashikur Rahman, Saeed Anwar, Muhammad Usman, Ajmal Mian
https://arxiv.org/abs/2406.09155

DefAn: Definitive Answer Dataset for LLMs Hallucination Evaluation
Large Language Models (LLMs) have demonstrated remarkable capabilities, revolutionizing the integration of AI in daily life applications. However, they are prone to hallucinations, generating claims that contradict established facts, deviating from prompts, and producing inconsistent responses when the same prompt is presented multiple times. Addressing these issues is challenging due to the lack of comprehensive and easily assessable benchmark datasets. Most existing datasets are small and rel…

DHL / Deutsche Post
Die Post mal wieder.
Seitdem ich in HH wohne kommen nur ~2/3 der Briefe an. Der Rest wird retourniert; inklusive Bankomatkarten und was-weiß-ich-nicht-alles. The unknown unknowns.
Jetzt dachte ich mir, hm, okay, bestell ich 'ne Briefankündigung — dann hab ich hinterher was für die Bundesnetzagentur zum Stunk machen. Die Post ignoriert das Problem auf Anfrage und Anschreiben immerhin geflissentlich.
Uuund was kommt seit zwei Wochen nicht an? Der Briefankündigungsfreischaltbrief.
..ich weiß auch nicht, was ich erwartet habe :D

ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures
Tobias Schimanski, Jingwei Ni, Roberto Spacey, Nicola Ranger, Markus Leippold
https://arxiv.org/abs/2406.09818

ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures
To handle the vast amounts of qualitative data produced in corporate climate communication, stakeholders increasingly rely on Retrieval Augmented Generation (RAG) systems. However, a significant gap remains in evaluating domain-specific information retrieval - the basis for answer generation. To address this challenge, this work simulates the typical tasks of a sustainability analyst by examining 30 sustainability reports with 16 detailed climate-related questions. As a result, we obtain a data…

Tree Search for Simultaneous Move Games via Equilibrium Approximation
Ryan Yu, Alex Olshevsky, Peter Chin
https://arxiv.org/abs/2406.10411 https://arxiv.org/pdf/2406.10411
arXiv:2406.10411v1 Announce Type: new
Abstract: Neural network supported tree-search has shown strong results in a variety of perfect information multi-agent tasks. However, the performance of these methods on partial information games has generally been below competing approaches. Here we study the class of simultaneous-move games, which are a subclass of partial information games which are most similar to perfect information games: both agents know the game state with the exception of the opponent's move, which is revealed only after each agent makes its own move. Simultaneous move games include popular benchmarks such as Google Research Football and Starcraft.
In this study we answer the question: can we take tree search algorithms trained through self-play from perfect information settings and adapt them to simultaneous move games without significant loss of performance? We answer this question by deriving a practical method that attempts to approximate a coarse correlated equilibrium as a subroutine within a tree search. Our algorithm works on cooperative, competitive, and mixed tasks. Our results are better than the current best MARL algorithms on a wide range of accepted baseline environments.

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?
Zhaochen Su, Juntao Li, Jun Zhang, Tong Zhu, Xiaoye Qu, Pan Zhou, Yan Bowen, Yu Cheng, Min zhang
https://arxiv.org/abs/2406.09072

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?
Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world. Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. In this paper, we introduce CoTempQA, a comprehensive co-temporal Question Answering (QA) benchmark containing four co-temporal scenarios (Equal, Overlap, During, Mix) with 4,748…