About
With a decade of experience in data engineering, data science and machine learning, I am…
Articles by Aaditya
Contributions
-
How do you evaluate the quality and accuracy of the texts generated by transformers and GPT-3 models?
Text generation brings great benefits but also faces challenges. It's a time-saver, quickly producing diverse content from stories to technical reports, and can even personalize material for specific audiences. However, it's not perfect. Sometimes, it misses the mark on complex contexts or nuanced topics, leading to errors. AI-written text might lack the originality and emotional depth of human writing. There's a risk of bias too, the model can be only as unbiased as its training data. It might generate convincing but inaccurate information, so fact-checking is crucial. In essence, while text generation tools are incredibly efficient and versatile, they need careful oversight to navigate their limitations in creativity, bias, and accuracy.
-
How do you evaluate the quality and accuracy of the texts generated by transformers and GPT-3 models?
GPT-3 generates text using autoregressive language modeling, where it predicts each word based on the preceding context, drawing from its vast training across varied texts. To choose contextually suitable words, it uses methods like greedy, beam, or top-k sampling, crafting text that is coherent and rich in context. A key feature of GPT-3's architecture is its attention mechanism. This allows the model to 'pay attention' to different parts of the input, not just the immediate previous words. It helps GPT-3 understand the entire input sequence, enabling it to handle nuances in language effectively. This attention to detail ensures that the generated text is not only grammatically correct but also stylistically adaptable.
-
How do you evaluate the quality and accuracy of the texts generated by transformers and GPT-3 models?
Transformers revolutionized neural networks with 'attention mechanisms', shifting away from traditional sequential processing models like RNNs and LSTMs. They analyze entire texts in one go, spotlighting key words and phrases for a comprehensive understanding. This makes them highly efficient in handling complex language tasks. GPT-3, a model by OpenAI, exemplifies this technology. Trained on vast internet text, it's akin to a supercharged autocomplete, capable of generating contextually rich and coherent text on almost any topic. From writing essays to mimicking styles, GPT-3's outputs are based on learned patterns from its extensive training.
Experience
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Education
-
-
-
Activities and Societies: Society of Automotive Engineers
Licenses & Certifications
Honors & Awards
-
PyData NYC 2023 : Self-Service Analytics using LLMs
-
-
Panelist at Cohen Veterans Care Summit 2018
-
Panel Discussion: Advancing Mental Healthcare using Data Science
-
Academic Excellence Award
Northeastern University
Languages
-
English
Native or bilingual proficiency
-
Hindi
Native or bilingual proficiency
-
Marathi
Native or bilingual proficiency
Recommendations received
2 people have recommended Aaditya
Join now to viewOther similar profiles
Explore collaborative articles
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
Explore MoreOthers named Aaditya Bhat
-
Aaditya Bhat
Chem-Ing @ Karlsruhe Institut für Technologie(KIT)
-
Aaditya Bhat
Student at Liberal Arts & Science Academy
-
Aaditya Bhat
PhD Candidate at DMSE MIT
-
Aaditya Bhat
10 others named Aaditya Bhat are on LinkedIn
See others named Aaditya Bhat