Honorable Mentions
Last updated
Last updated
While Prompt engineering and retrieval-augmented generation (RAG) are among the best techniques to tackle the challenges of large language models (LLMs), there are still other concepts that are worth mentioning. These methods address limitations like resource inefficiency, knowledge gaps, and bias.
Post-processing involves modifying or enhancing a model’s outputs after they are generated. This method works independently of the training process, making it a lightweight and versatile solution.
Text Refinement: Algorithms analyze the structure, tone, and grammar of generated outputs, applying corrections as needed. For instance, a grammar checker integrated into a post-processing pipeline ensures clarity and professionalism in customer-facing communications.
Bias Mitigation: Bias detection systems assess the output for potentially harmful or discriminatory content. A re-ranking mechanism or replacement algorithm swaps biased phrases with neutral or fair alternatives.
Fact-Checking: Post-processing tools link outputs to external fact databases or retrieval systems. For example, a generated claim can be verified by querying a knowledge base, and corrections are applied if inconsistencies are found.
Post-processing is a lightweight solution that often complements more involved methods like fine-tuning or retrieval-augmented generation.
Many LLMs struggle with keeping up-to-date information or domain-specific expertise. Knowledge injection introduces external information into a model, either dynamically during inference or statically through pre-processing.
Dynamic Injection: External APIs or real-time databases can be queried during inference, allowing the model to access the latest information or specialized knowledge. For example, a financial assistant could fetch live stock data while generating a report.
Embedding External Knowledge: Knowledge graphs or structured datasets can be integrated into the training or inference pipeline. This approach enhances the model’s ability to generate accurate and context-aware responses.
Knowledge injection ensures that LLMs can address knowledge gaps without requiring a full retraining cycle.
Knowledge distillation simplifies large models by transferring their knowledge into smaller, more efficient versions. This process preserves the original model's capabilities while significantly reducing computational overhead.
Teacher-Student Framework:
The teacher model generates predictions or probabilities (soft labels) on a given dataset.
The student model is trained to mimic these outputs, learning from both the original dataset and the teacher’s behavior.
For example, if the teacher assigns probabilities of 0.6, 0.3, and 0.1 to three classes, the student learns not only the correct answer but also the uncertainty distribution.
Loss Function Adjustments:
The training process incorporates a distillation loss, which aligns the student’s outputs with the teacher’s. This allows the student to replicate the nuanced decision-making of the teacher.
Distilled models maintain performance while significantly reducing computational requirements. This makes them suitable for deployment on devices with limited resources, such as smartphones or embedded systems.
Ensemble methods combine multiple models to improve robustness and accuracy. Instead of relying on a single model, this approach aggregates the strengths of several, producing more accurate and reliable results.
Voting Systems: Each model in the ensemble generates its prediction. A majority vote or weighted average determines the final output. For example, in a sentiment analysis task, three models might predict "0", "1", and "0". The ensemble output would be "0" based on the majority.
Model Specialization: Different models handle different aspects of the task. For instance, one model might focus on grammar while another specializes in fact-checking. Their outputs are then combined to generate a final result.
Diversity Maximization: Diverse models with varying architectures or training datasets are used to reduce the likelihood of shared errors (models making the same mistake).
Ensemble models shine in scenarios requiring high reliability or where multiple objectives must be addressed simultaneously.
