Remove 2019 Remove Knowledge Model Remove Natural Language Processing
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Commonsense Reasoning for Natural Language Processing

Probably Approximately a Scientific Blog

Figure 1: adversarial examples in computer vision (left) and natural language processing tasks (right). Machine learning models today perform reasonably well on perception tasks (image and speech recognition). Representing symbolic knowledge as vectors: Lin et al. Image credit: Lin et al. Image credit: Lin et al.

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Anthropic Claude 3.5 Sonnet ranks number 1 for business and finance in S&P AI Benchmarks by Kensho

AWS Machine Learning Blog

In the following example, for an LLM to answer the question correctly, it needs to understand the table row represents location and the column represents year, and then extract the correct quantity (total amount) from the table based on the asked location and year: Question : What was the Total Americas amount in 2019?