Tootfinder

Some developers are releasing longer context windows for Llama 3 thanks to Meta's open-source approach; Llama 3 has a short context window of 8,000 tokens (Kalley Huang/The Information)
https://www.theinformation.com/articles/how-developers-gav…

How Developers Gave Llama 3 More Memory
Developers have been praising Meta Platforms’ Llama 3, the latest version of its flagship large language model. But, as my colleague Stephanie and I explained, they have one big criticism: Llama 3’s context window is too short, at just over 8,000 tokens. (As a refresher, a context window is how ...

Context-Aware Clustering using Large Language Models
Sindhu Tipirneni, Ravinarayana Adkathimar, Nurendra Choudhary, Gaurush Hiranandani, Rana Ali Amjad, Vassilis N. Ioannidis, Changhe Yuan, Chandan K. Reddy
https://arxiv.org/abs/2405.00988

Context-Aware Clustering using Large Language Models
Despite the remarkable success of Large Language Models (LLMs) in text understanding and generation, their potential for text clustering tasks remains underexplored. We observed that powerful closed-source LLMs provide good quality clusterings of entity sets but are not scalable due to the massive compute power required and the associated costs. Thus, we propose CACTUS (Context-Aware ClusTering with aUgmented triplet losS), a systematic approach that leverages open-source LLMs for efficient and…

"...şer olmazsa bilinmez hayır..."
https://open.spotify.com/track/4ZuHxg0IbPMkzdzroShXqg?si=GZlOHol2TeulX06QYXzTqg&context=spotify:album:3JgHixryi9xP4ZJ7drCXQk

Artificial intelligence for context-aware visual change detection in software test automation
Milad Moradi, Ke Yan, David Colwell, Rhona Asgari
https://arxiv.org/abs/2405.00874

Artificial intelligence for context-aware visual change detection in software test automation
Automated software testing is integral to the software development process, streamlining workflows and ensuring product reliability. Visual testing within this context, especially concerning user interface (UI) and user experience (UX) validation, stands as one of crucial determinants of overall software quality. Nevertheless, conventional methods like pixel-wise comparison and region-based visual change detection fall short in capturing contextual similarities, nuanced alterations, and underst…

ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models
Vishnunandan L. N. Venkatesh, Byung-Cheol Min
https://arxiv.org/abs/2404.02318

ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models
Incorporating language comprehension into robotic operations unlocks significant advancements in robotics, but also presents distinct challenges, particularly in executing spatially oriented tasks like pattern formation. This paper introduces ZeroCAP, a novel system that integrates large language models with multi-robot systems for zero-shot context aware pattern formation. Grounded in the principles of language-conditioned robotics, ZeroCAP leverages the interpretative power of language models…

Mehrfachnennungen:
https://open.spotify.com/track/01Rh0MPfAg0twrtZfujv4R?si=EgxMQfAXS9Gcuu63nlbWrQ&context=spotify:album:6pZCVmEIaBAK9cnX9gMMnh

"In-Context Learning" or: How I learned to stop worrying and love "Applied Information Retrieval"
Andrew Parry, Debasis Ganguly, Manish Chandra
https://arxiv.org/abs/2405.01116

"In-Context Learning" or: How I learned to stop worrying and love "Applied Information Retrieval"
With the increasing ability of large language models (LLMs), in-context learning (ICL) has evolved as a new paradigm for natural language processing (NLP), where instead of fine-tuning the parameters of an LLM specific to a downstream task with labeled examples, a small number of such examples is appended to a prompt instruction for controlling the decoder's generation process. ICL, thus, is conceptually similar to a non-parametric approach, such as $k$-NN, where the prediction for each instanc…

I was today years old when I learned that context receivers have been tossed in favor of https://github.com/Kotlin/KEEP/blob/context-parameters/proposals/context-parameters.md

KEEP/proposals/context-parameters.md at context-parameters · Kotlin/KEEP
Kotlin Evolution and Enhancement Process. Contribute to Kotlin/KEEP development by creating an account on GitHub.

Apple researchers detail an AI system that can resolve ambiguous references to on-screen entities, in some cases better than GPT-4 can, and can run "on-device" (Michael Nuñez/VentureBeat)
https://venturebeat.com/ai/apple-resea

Apple researchers develop AI that can ‘see’ and understand screen context
Apple researchers develop AI system that understands screen references and context, enabling more natural voice assistant interactions.

This https://arxiv.org/abs/2403.20147 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context
The pervasive influence of social biases in language data has sparked the need for benchmark datasets that capture and evaluate these biases in Large Language Models (LLMs). Existing efforts predominantly focus on English language and the Western context, leaving a void for a reliable dataset that encapsulates India's unique socio-cultural nuances. To bridge this gap, we introduce IndiBias, a comprehensive benchmarking dataset designed specifically for evaluating social biases in the Indian con…