All 1z0-1127-24 Test Inside Oracle Questions

In natural language processing (NLP), encoder and decoder models play distinct but complementary roles:

Encoder Models: These models convert a sequence of words into a vector representation. They capture the semantic meaning of the input text and encode it into a fixed-size vector.

Decoder Models: These models take the vector representation generated by the encoder and convert it back into a sequence of words. This process allows for generating new text based on the encoded information, such as in translation or text generation tasks.

References

Research articles on encoder-decoder architectures in NLP

Technical guides on the use of encoder and decoder models in machine translation and text generation

In LangChain, the "mmr" (Maximal Marginal Relevance) search type is used to balance between relevancy and diversity when retrieving documents. This technique aims to select documents that are not only relevant to the query but also diverse from each other. This helps in avoiding redundancy and ensures that the retrieved set of documents covers a broader aspect of the topic.

Maximal Marginal Relevance (MMR) works by iteratively selecting documents that have high relevance to the query but low similarity to the documents already selected. This ensures that each new document adds new information and perspectives, rather than repeating what is already included.

References

LangChain documentation on retrievers and search types

Research papers and articles on Maximal Marginal Relevance (MMR)

The "Top p" parameter, also known as nucleus sampling, in generative AI models limits token selection based on the sum of their probabilities. It ensures that the cumulative probability of the selected tokens meets or exceeds a specified threshold p. This approach dynamically includes as many tokens as necessary to reach the desired probability sum, allowing for more diverse and contextually appropriate outputs compared to a fixed top-k selection.

References

Research papers on nucleus sampling and token selection methods

OCI Generative AI model documentation

Using vector databases with Large Language Models (LLMs) offers cost-related benefits, particularly by providing real-time updated knowledge bases. This approach can be more cost-effective than fine-tuning LLMs frequently, as vector databases allow for the dynamicretrieval of information without the need for constant retraining. This reduces operational costs while maintaining access to up-to-date data.

References

Articles on the cost efficiency of vector databases

Research on integrating vector databases with LLMs for real-time updates