Keyword-Based Search Evaluation

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Questions and Answers

What is a key difference between fine-tuning and PEFT?

Fine-tuning updates all parameters, while PEFT only updates some. (correct)

Fine-tuning is less computationally intensive than PEFT.

PEFT needs to train from scratch on new data, unlike fine-tuning.

PEFT requires more labeled data compared to fine-tuning.

What does accuracy measure in the context of fine-tuning results for a generative model?

The proportion of incorrect predictions made by the model.

The total predictions made by the model, correct or not.

The average confidence level of the predictions made.

The number of predictions made correctly out of all predictions. (correct)

In the context of generating text with a Large Language Model, what does greedy decoding entail?

Choosing the word with the highest probability at each decoding step. (correct)

Using a random selection from the entire vocabulary at each step.

Selecting a word without regard to its probability.

Considering contextual information before selecting any word.

What is the role of indexing in managing and querying vector data?

To map vectors to a data structure for faster searching. Signup and view all the answers

When does a chain typically interact with memory in the LangChain framework?

After user input but before execution, and again after core logic but before output. Signup and view all the answers

What type of data does fine-tuning predominantly require?

Labeled data tailored for the model. Signup and view all the answers

In PEFT, how are parameter updates handled compared to traditional fine-tuning?

PEFT selectively updates a small subset of parameters. Signup and view all the answers

Which of the following statements about the evaluation of generative models is true?

Only the correct predictions count towards accuracy. Signup and view all the answers

How are documents usually evaluated in the simplest form of keyword-based search?

Based on the presence and frequency of the user-provided keywords Signup and view all the answers

When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

When the LLM does not perform well on a task and the data for prompt engineering is too large Signup and view all the answers

In which scenario is soft prompting appropriate compared to other training styles?

When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training Signup and view all the answers

How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

Increasing the temperature flattens the distribution, allowing for more varied word choices. Signup and view all the answers

Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

PEFT requires replacing the entire model architecture with a new one designed specifically for the new task. Signup and view all the answers

What is the primary advantage of using fine-tuning on an LLM?

It allows for adaptability to new tasks without starting from scratch. Signup and view all the answers

In the context of LLMs, what is the primary function of soft prompting?

To provide a mechanism for adjustment without extensive retraining. Signup and view all the answers

What effect does decreasing temperature have on the decoding process of LLMs?

It produces more predictable outputs. Signup and view all the answers

Study Notes

Keyword-Based Document Evaluation

Documents are primarily evaluated based on the presence and frequency of user-provided keywords.

Fine-Tuning Large Language Models (LLM)

Fine-tuning is suitable when the LLM does not perform well on a task and when data for prompt engineering is too vast.
It allows the model to access the latest data for improved output generation.

Soft Prompting

Soft prompting is advantageous when adapting a model to perform in a new domain not covered in its original training.
It adds learnable parameters to a LLM without requiring task-specific training.

Temperature Setting in Decoding Algorithms

Increasing temperature flattens the probability distribution, promoting more diverse word choices.
Decreasing temperature narrows the distribution, favoring more likely words.

Fine-tuning vs. Parameter-Efficient Fine-Tuning (PEFT)

Fine-tuning involves training the entire model on new data, leading to high computational costs.
PEFT updates only a small subset of parameters, thus minimizing data requirements and computational load.

Accuracy Measurement in Generative Models

Accuracy reflects the proportion of correct predictions made by the model during evaluation.

Greedy Decoding in Text Generation

Greedy decoding involves selecting the word with the highest probability at each decoding step.

Indexing in Vector Data Management

Indexing maps vectors to a data structure, allowing for rapid searching and efficient retrieval.

Memory Interaction in LangChain Framework

A chain interacts with memory after user input but before chain execution and again after core logic before producing output.

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Description

This quiz explores the evaluation methods for documents in keyword-based search scenarios. It covers aspects such as keyword presence, document length, and the use of multimedia elements. Test your understanding of how these factors influence search outcomes!