Program 4
AIM:
Use word embeddings to improve prompts for Generative AI
model. Retrieve similar words using word embeddings. Use the similar words to
enrich a GenAI prompt. Use the AI model to generate responses for the original
and enriched prompts. Compare the outputs in terms of detail and relevance.
Theory:
Prompt Enrichment using Word Embeddings
Word embeddings are a technique in Natural Language
Processing (NLP) that represent words as numerical vectors based on their
meaning and context. Words with similar meanings have similar vector
representations. Popular models include Word2Vec and GloVe.
In this experiment, word embeddings are used to improve
prompts given to a Generative AI model. First, a basic prompt is taken as
input. Then, important words from the prompt are identified, and their
semantically similar words are retrieved using a pre-trained embedding model.
These similar words are added to the original prompt to
create an enriched prompt, which provides more context and detail. Both the
original and enriched prompts are then given to a Generative AI model (such as
FLAN-T5 or GPT-based models) to generate responses.
The outputs are compared in terms of:
• Detail
(amount of information provided)
• Relevance
(how well the response matches the topic)
It is observed that enriched prompts generally produce more
detailed, informative, and contextually relevant responses, as the additional
semantic information helps the model better understand the user’s intent.
Program
Step 1: Install
Libraries
Step 2: Load Word
Embeddings (GloVe)
Step 3: Get
Similar Words
Step 4: Enrich
Prompt
Step 5: Load Free
LLM (FLAN-T5)
Step 6: Generate
Outputs
Step 7: Quantitative Comparison
1.
Length-Based Detail
2.
Relevance using Semantic Similarity
3.
Diversity (Unique Words)
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