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5 papers from Allen Institute for AI (AI2) on Natural Language Processing
Despite their increasing role in scientific discovery, today's AI models are surprisingly bad at predicting which scientific breakthroughs will actually happen and when.
Skip the annotation bottleneck: ScheMatiQ lets you turn research questions and text corpora into structured databases with LLMs, guided by a simple web interface.
Synthetic benchmarks can't catch the nuances of personalized deep research, as real users revealed nine critical errors that LLM judges missed entirely.
AI is poised to automate the most joyful and agentic parts of our jobs, while developers are building AI with the wrong traits.
LLMs still struggle with factual accuracy in specialized medical domains like pancreatic cancer, with hallucination rates varying wildly and web search integration failing to guarantee better responses.