𝐄𝐩𝐢𝐬𝐨𝐝𝐞 𝟏𝟎: 𝐅𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 & 𝐆𝐞𝐧𝐞𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧

📅 Published on September 25, 2024

In a business environment, the goal is to produce an LLM that can understand, respond or automate tasks . Each business is unique, so to capture most of the necessary knowledge, you need to achieve what I call “Generalization.”

In modern business environments, the goal of developing a language model (LLM) is to enable it to understand, respond, or automate tasks effectively (with an agent/backend, cf Episode 9), you need to achieve what we call “Generalization”. Achieving this requires a model that can generalize to most business scenarios, even if specific prompts are not part of its training data. This article explores various strategies used to improve LLM adaptability, including LoRa (Low-Rank Adaptation), Direct Preference Optimization (DPO), Reinforcement Learning with Human Feedback (RLHF), and Reinforcement Learning with Machine Feedback (RLMF).

I. Generalization in LLMs

Generalization occurs when a model has been fine-tuned enough to adapt to various business scenarios while maintaining its intended purpose. This adaptability is crucial for creating robust, scalable solutions that handle tasks with limited training data.

II. LoRa (Low-Rank Adaptation)

LoRa is a strategy that speeds up proof-of-concept development, often delivering results within weeks. However, LoRa is more of an indexing method rather than “traditional sense”. It modifies the linear projection matrices (Wq, Wk, Wv) responsible for generating query, key, and value matrices in the attention mechanism. The advantage of LoRa is that it allows you to create a proof of concept very quickly, often within weeks.

While LoRa offers speed, it falls short in achieving the level of generalization required for handling diverse and complex scenarios. To overcome this limitation, I’ve developed a new version of LoRa, enhanced with Artificial Neural Networks (ANN) algorithms. This upgraded version goes beyond simple Question/Answer datasets, enabling LoRa to tackle more intricate queries and adapt effectively to varying contexts.

The downside is that it does not achieve Generalization. To address this issue, we developed a new version of LoRa using ANN algorithms, enabling it to handle more complex queries beyond a simple Question/Answer dataset and adapt effectively to the context. We will give the results in a few weeks.

The second issue is that you need to anticipate every interaction with the LLM since the dataset is based on predefined Question/Answer pairs.

III. 𝐃𝐏𝐎 (Direct Preference Optimization) / 𝐑𝐋𝐇𝐅 (Reinforcement Learning Human Feedback) / 𝐑𝐋𝐌𝐅 (Reinforcement Learning Machine Feedback)

Unlike LoRa, Direct Preference Optimization (DPO) and Reinforcement Learning techniques directly modify the neural network. DPO refines the model by optimizing its outputs to align with user preferences, ensuring that the LLM behaves according to business-specific needs.

Reinforcement Learning with Human Feedback (RLHF) leverages human input to fine-tune the model’s responses. By adjusting the network’s weights based on human evaluations, RLHF improves the model’s ability to align with human expectations. This feedback loop helps the LLM become more intuitive and relevant in real-world applications.

However, RLHF has limitations due to the manual effort required for prompt evaluation. To address this challenge, we created Reinforcement Learning with Machine Feedback (RLMF). RLMF utilizes uses a dual-model approach with a generator and a discriminator. The discriminator analyzes interactions and creates datasets to improve the generator, enabling the system to refine its responses and adapt dynamically based on machine-generated feedback.

IV. Impact on model generalization

LoRa, DPO, RLHF, and RLMF differ significantly in how they impact model generalization. While LoRa focuses on modifying specific attention matrices, DPO, RLHF, and RLMF affect the entire neural network, enabling more profound learning and broader adaptability. These latter approaches are essential for producing models capable of handling diverse business scenarios, leading to better generalization.

Disclaimer: The image below represents an oversimplified transformer architecture. It is intended to illustrate the differences between LoRA and DPO/RLHF. Please note that real transformer models are much more complex, involving multiple stacked layers of attention and feed-forward networks, multi-head attention mechanisms, positional encoding, as well as layer normalization and residual connections. Each model has its own unique strategy and architecture.