Major Issues

[vishesh9131]

1. Repetitiveness

Problem: The text contains many repeated phrases and words, reducing readability.
Solution:

• Use a Penalty for Repeated Words: Implement a repetition penalty during generation to discourage the model from producing the same words or phrases repeatedly.
• Diverse Beam Search: Use a diverse beam search algorithm to generate more varied outputs.
• Post-Processing: Detect and remove repeated phrases using simple heuristics or regular expressions.

2. Lack of Context and Structure

Problem: The text lacks logical flow and context, making it difficult to follow.
Solution:

• Structured Prompts: Use more structured prompts that provide clear context and guide the model towards producing more coherent text.
• Fine-Tuning with Structured Data: Fine-tune the model on datasets that have well-structured and coherent text to help it learn better text generation patterns.
• Template-Based Generation: Use templates or predefined structures to guide the model in generating more organized content.

3. Inconsistent Tone

Problem: The text shifts in tone and style unpredictably.
Solution:

• Consistent Training Data: Ensure that the training data maintains a consistent tone and style.
• Fine-Tuning: Fine-tune the model on a dataset that matches the desired tone and style.
• Post-Processing: Apply post-processing rules to enforce tone consistency, such as adjusting certain phrases or words to match the desired tone.

4. Grammatical Issues

Problem: The text contains grammatical errors and awkward phrasings.
Solution:

• Grammar Checking Tools: Integrate grammar checking tools like Grammarly or LanguageTool in the post-processing stage to correct grammatical errors.
• Fine-Tuning on High-Quality Data: Fine-tune the model on datasets with high grammatical quality.
• Data Augmentation: Use data augmentation techniques to correct grammar in the training data, such as back-translation or grammar correction models.

5. Fragmentation

Problem: The text contains many fragmented sentences and disconnected ideas.
Solution:

• Coherence Training: Train or fine-tune the model with a focus on coherence, using datasets that emphasize connected and logically flowing ideas.
• Longer Context Window: Increase the context window size during training and generation to help the model maintain coherence over longer passages.
• Post-Processing for Coherence: Implement post-processing rules that merge fragments and ensure logical connections between ideas.

6. Redundancy

Problem: The text is overly redundant, making it monotonous.
Solution:

• Repetition Penalty: Apply a repetition penalty during text generation to reduce redundant words and phrases.
• Paraphrasing Models: Use paraphrasing models to rewrite redundant segments of the text.
• Diverse Training Data: Ensure the training data contains diverse expressions and vocabulary to encourage the model to use varied language.

Author : @vishesh9131