Tootfinder

Pentagon confirms 'capability review' amid pause in aid deliveries to Ukraine: https://benborges.xyz/2025/07/03/pentagon-confirms-capability-review-amid.html

https://www.reuters.com/world/asia-pacific/australias-qantas-confirms-cyber-incident-one-its-contact-centres-2025-07-01/
Australia's Qantas says 6 million customer accounts accessed in cyber hack

Enhancing Customer Service Chatbots with Context-Aware NLU through Selective Attention and Multi-task Learning
Subhadip Nandi, Neeraj Agrawal, Anshika Singh, Priyanka Bhatt
https://arxiv.org/abs/2506.01781

Enhancing Customer Service Chatbots with Context-Aware NLU through Selective Attention and Multi-task Learning
Customer service chatbots are conversational systems aimed at addressing customer queries, often by directing them to automated workflows. A crucial aspect of this process is the classification of the customer's intent. Presently, most intent classification models for customer care utilise only customer query for intent prediction. This may result in low-accuracy models, which cannot handle ambiguous queries. An ambiguous query like "I didn't receive my package" could indicate a delayed order, …

ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data
Yu Zhang, Ruijie Yu, Jidong Tian, Feng Zhu, Jiapeng Liu, Xiaokang Yang, Yaohui Jin, Yanyan Xu
https://arxiv.org/abs/2506.23520

ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data
With the increasing interest in robotic synthesis in the context of organic chemistry, the automated extraction of chemical procedures from literature is critical. However, this task remains challenging due to the inherent ambiguity of chemical language and the high cost of human annotation required for developing reliable computer-aided extraction protocols. Here, we present ChemActor, a fully fine-tuned large language model (LLM), as a chemical executor to convert between unstructured experim…

sp_hypertext: Hypertext 2009 dynamic contact network
The temporal network of contacts among attendees of the ACM Hypertext 2009 conference, which spanned 2.5 days of time.
This network has 113 nodes and 2196 edges.
Tags: Social, Offline, Unweighted, Temporal
https://networks.skewed.de/net/sp_hype…

Eka-Eval : A Comprehensive Evaluation Framework for Large Language Models in Indian Languages
Samridhi Raj Sinha, Rajvee Sheth, Abhishek Upperwal, Mayank Singh
https://arxiv.org/abs/2507.01853

Eka-Eval : A Comprehensive Evaluation Framework for Large Language Models in Indian Languages
The rapid advancement of Large Language Models (LLMs) has intensified the need for evaluation frameworks that go beyond English centric benchmarks and address the requirements of linguistically diverse regions such as India. We present EKA-EVAL, a unified and production-ready evaluation framework that integrates over 35 benchmarks, including 10 Indic-specific datasets, spanning categories like reasoning, mathematics, tool use, long-context understanding, and reading comprehension. Compared to e…

Il datore di lavoro può raccogliere i log di navigazione e i metadati delle mail dei dipendenti solo in presenza di specifiche condizioni e garanzie.
Il provvedimento del #GarantePrivacy è stato deliberato al termine di un ciclo ispettivo sui trattamenti dei dati dei dipendenti

Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla
Md Sazzadul Islam Ridoy, Sumi Akter, Md. Aminur Rahman
https://arxiv.org/abs/2507.01931

Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla
In recent years, neural models trained on large multilingual text and speech datasets have shown great potential for supporting low-resource languages. This study investigates the performances of two state-of-the-art Automatic Speech Recognition (ASR) models, OpenAI's Whisper (Small & Large-V2) and Facebook's Wav2Vec-BERT on Bangla, a low-resource language. We have conducted experiments using two publicly available datasets: Mozilla Common Voice-17 and OpenSLR to evaluate model performances. Th…

LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing
Daniel Fein, Sebastian Russo, Violet Xiang, Kabir Jolly, Rafael Rafailov, Nick Haber
https://arxiv.org/abs/2507.00769

LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing
Evaluating creative writing generated by large language models (LLMs) remains challenging because open-ended narratives lack ground truths. Without performant automated evaluation methods, off-the-shelf (OTS) language models are employed as zero-shot judges, yet their reliability is unclear in this context. In pursuit of robust evaluation for creative writing, we introduce LitBench, the first standardized benchmark and paired dataset for creative writing verification, comprising a held-out test…

MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation
Yile Liu, Ziwei Ma, Xiu Jiang, Jinglu Hu, Jing Chang, Liang Li
https://arxiv.org/abs/2506.01776

MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation
With the rapid adoption of large language models (LLMs) in natural language processing, the ability to follow instructions has emerged as a key metric for evaluating their practical utility. However, existing evaluation methods often focus on single-language scenarios, overlooking the challenges and differences present in multilingual and cross-lingual contexts. To address this gap, we introduce MaXIFE: a comprehensive evaluation benchmark designed to assess instruction-following capabilities a…