AI has made significant development, and LLMs are proof of it. Large language models (LLMs) are known for their remarkable ability to generate human-like text in any language, even the programming languages, for any query within minutes. However, what exactly is an LLM?
LLMs are a type of neural network that consists of an encoder and decoder with self-attention capabilities. The encoder and decoder work on extracting the meaning from a sequence of text to understand their relationship. Then, it phrases them to create a meaningful text.
LLMs are based on a specific type of neural network architecture which is called a transformer, and it is designed to process and generate data in sequence like text. They are capable of unsupervised learning, but the emphasis on self-learning is more, so the transformer can learn and understand grammar, languages, and knowledge.
LLMs are trained to have vast knowledge but lack specialization in certain areas. This is where fine-tuning comes in: to make the model learn domain-specific data so the LLM can provide accurate and precise output for target applications.
Understanding Fine-Tuning
Fine-tuning focuses on adjusting the LLM parameters, and this adjustment depends upon the specific task that you want to fulfill. Here are some common approaches that help to fine-tune the LLMs:
Feature Extraction: It is the primary approach, where the final layer is trained on a task-specific task, but the rest of the model remains frozen.
Full Fine-Tuning: Unlike feature extraction, where only the final layer is trained, the entire model is trained on task-specific data. This is done when task-specific data is large and different from pre-trained data.
Supervised Fine-Tuning: This is a type of prominent fine-tuning where the model is trained on a specific task-labeled data, where each input data point is associated with a correct output. This fine-tuning approach guides the model in learning how to adjust its parameters so it can accurately predict labels. There are five techniques through which models undergo supervised fine-tuning: basic hyperparameter tuning, transfer learning, multi-task learning, few-shot learning, and task-specific fine-tuning.
Reinforcement Learning from Human Feedback: This is an innovative fine-tuning method in which language models are trained through interaction with human feedback. This method introduces the human element in the fine-tuning process to adapt and evolve on the basis of real-world feedback.
How is RLHF the Preferred Approach?
RLHF is the preferred approach in specific applications where human expertise or interference is required to help the model learn what is applicable and accepted in the real world. For instance, in the case of legal documentation, it is not acceptable to depend upon the LLM’s flexibility that supports the creation of more vivid and imaginative results.
RLHF trains the model on real-life parameters, and the human feedback aligns it to produce realistic outputs. It makes the model capable of mimicking human interactions, understanding them, and adjusting the outputs accordingly.
Different types of RLHF techniques are used to train the models based on human feedback:
Reward Modelling
This is a type of learning method where the model produces a list of outputs, and a human ranks them based on their quality. The model learns to predict the human-provided rewards and adjusts its behavior to maximize the predicted rewards.
Proximal Policy Optimization
This is an iterative algorithm where the changes or upgrades the language model’s policy to maximize the expected reward. The main philosophy behind this method is to improvise the policy while ensuring there are no drastic changes made from the previous policy.
Comparative Ranking
Comparative ranking is similar to reward modeling, but the model learns from relative rankings of multiple outputs by human evaluators. The model adjusts its behavior to adjust the output generation on the basis of high-ranking outputs.
Preference Learning
Preference learning is a type of fine-tuning method where human evaluators give comprehensive feedback in the form of preferences between states, actions, or trajectories.
Parameter Efficient Fine-Tuning
This is used to improve the performance of pre-trained LLMs on specific downstream tasks while reducing the amount of trainable parameters.
Importance of the Human Element in Fine-Tuning
While the world is progressing towards automation, and LLMs automate the process of creation of content and images, humans need to take the upper hand in training and fine-tuning the LLMs. From selecting the relevant datasets to designing protocols and providing feedback, humans act as a guiding light to train the models to give the right outputs.
There are different LLM benchmarks, like:
- BLEU for translation accuracy
- ROUGE for summarization
- Perplexity scores for overall linguistic complexity
However, these metrics only provide a slice of insights and don’t provide a holistic viewpoint. This ensures that models generate not only grammatically correct outputs but also meaningful, related to context, and ethical outputs.
The Bottom Line,
Human evaluators are trained to find the inconsistencies that the automated benchmarks are sure to overlook. There are chances that a language model creates grammatically correct content but skews the narration like the content is inadvertently supporting bias. Hence, the human evaluator not only ensures that the output is contextual but is also ethical and doesn’t support bias in any case.
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