Tootfinder

Mary Poppins’ UK age rating raised to PG due to discriminatory language;
British Board of Film Classification lifts it from U certificate almost 60 years after film was first released
https://www.theguardian.com/film/2024/feb/

And another important assessment of the response to the abusive catastrophe unfolding in Gaza:
https://www.theguardian.com/commentisfree/2024/apr/25/pro-palestine-israel-language-conflict?CMP=share_btn_url

And another important assessment of the response to the abusive catastrophe unfolding in Gaza:
https://www.theguardian.com/commentisfree/2024/apr/25/pro-palestine-israel-language-conflict?CMP=share_btn_url

British film censors change the age rating for the classic 1964 film Mary Poppins from U to PG citing "discriminatory language" (Helen Bushby/BBC)
https://www.bbc.com/news/entertainment-arts-68402362

Mary Poppins film age rating raised over 'discriminatory language'
The classic film is changed from a U to a PG rating because it features "discriminatory language".

ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching
Youpeng Zhao, Di Wu, Jun Wang
https://arxiv.org/abs/2403.17312 https://

ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching
The Transformer architecture has significantly advanced natural language processing (NLP) and has been foundational in developing large language models (LLMs) such as LLaMA and OPT, which have come to dominate a broad range of NLP tasks. Despite their superior accuracy, LLMs present unique challenges in practical inference, concerning the compute and memory-intensive nature. Thanks to the autoregressive characteristic of LLM inference, KV caching for the attention layers in Transformers can eff…

Safe Task Planning for Language-Instructed Multi-Robot Systems using Conformal Prediction
Jun Wang, Guocheng He, Yiannis Kantaros
https://arxiv.org/abs/2402.15368

Safe Task Planning for Language-Instructed Multi-Robot Systems using Conformal Prediction
This paper addresses task planning problems for language-instructed robot teams. Tasks are expressed in natural language (NL), requiring the robots to apply their capabilities (e.g., mobility, manipulation, and sensing) at various locations and semantic objects. Several recent works have addressed similar planning problems by leveraging pre-trained Large Language Models (LLMs) to design effective multi-robot plans. However, these approaches lack mission performance and safety guarantees. To add…

New paper: Semantic differences in visually similar face emojis 📝
We show that even #emojis that look similar (like 😊, ☺️) have subtle, reliable meaning differences, supporting a lexicalist approach. #linguistics

KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion
Yilin Wang, Minghao Hu, Zhen Huang, Dongsheng Li, Dong Yang, Xicheng Lu
https://arxiv.org/abs/2403.17532

KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion
The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate. Recently, generative large language models (LLMs) have shown outstanding performance on several tasks such as information extraction and dialog systems. Leveraging them for KGC re-ranking is beneficial for leveraging the extensive pre-trained knowledge and powerful generative c…

🚨 Reminder to submit your best work on #Ukrainian to the 3rd Ukrainian Natural Language Processing workshop! #nlproc #callForPapers
Extended deadline: March 4!

Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model
Yongqi Zhao, Wenbo Xiao, Tomislav Mihalj, Jia Hu, Arno Eichberger
https://arxiv.org/abs/2404.16147

Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model
The advent of Large Language Models (LLM) provides new insights to validate Automated Driving Systems (ADS). In the herein-introduced work, a novel approach to extracting scenarios from naturalistic driving datasets is presented. A framework called Chat2Scenario is proposed leveraging the advanced Natural Language Processing (NLP) capabilities of LLM to understand and identify different driving scenarios. By inputting descriptive texts of driving conditions and specifying the criticality metric…