LIVE PULSE
3.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.1 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.7 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src3.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.1 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.7 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src
HEATPULSEAI MAGAZINES
FLIP · FOLLOW · SAVE
Log inSign up

Urdu

topic2 events
papersSEP 12 04:00 UTC

Study Examines Multilingual LLM Weaknesses in Urdu and Low-Resource Languages

A new arXiv paper investigates how well multilingual large language models handle open-ended text generation in Urdu, a low-resource language. The authors argue that models marketed as multilingual often fall short in cultural and linguistic correctness outside high-resource languages. The work contributes to broader questions about the reliability of these systems for non-English users.

multilingual/llm/low-resource/evaluation
papersSEP 10 04:00 UTC

Study introduces 'Missed-in-Urdu' scores to measure LLM hate speech detection gaps

A new arXiv paper examines how large language models detect hate speech in Urdu, a language with roughly 246 million speakers that the authors say has been largely overlooked in mainstream AI safety evaluation, including nine years of the Workshop on Online Abuse and Harms. The researchers propose 'Missed-in-Urdu' scores to quantify inconsistencies in how safety systems treat equivalent content across scripts and languages. The preprint appears in both the AI and computational linguistics categories on arXiv.

safety/multilingual/hate-speech/evaluation