AIF-C01 · Question #40
A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers. After multiple…
The correct answer is B. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms. Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex…
Question
A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers. After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers. How can the company improve the performance of the chatbot?
Options
- AUse few-shot prompting to define how the FM can answer the questions.
- BUse domain adaptation fine-tuning to adapt the FM to complex scientific terms.
- CChange the FM inference parameters.
- DClean the research paper data to remove complex scientific terms.
How the community answered
(21 responses)- A19% (4)
- B67% (14)
- C5% (1)
- D10% (2)
Explanation
Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex terminology, improving its performance on specialized tasks.
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