LLMs Alignment, Fine-tuning, RAG // most popular techniques
There are some controversial questions without a clear answer, with many options to consider.
Despite complex and expensive processes, LLMs tend to make errors (hallucinations), and there is no clear solution for it.
This blog post introduces different methods for adapting LLMs to specific domain data. The post explains key adaptation approaches, including pre-training, continued pre-training, fine-tuning, retrieval-augmented generation (RAG), and in-context learning (ICL). It advises against resource-intensive methods like pre-training and continued pre-training for teams with limited resources. Instead, it recommends fine-tuning, particularly parameter-efficient fine-tuning (PEFT), and explores the advantages of RAG and ICL for cost-effective adaptation. The post concludes with a flowchart to help teams choose the best method for their needs.
Instruction Fine-tuning (IFT) is a crucial phase in building large language models (LLMs). Previous works mainly focus on the IFT’s role in the transfer of behavioral norms and the learning of additional world knowledge. However, the understanding of the underlying mechanisms of IFT remains significantly limited. In this paper, we design a knowledge intervention framework to decouple the potential underlying factors of IFT, thereby enabling individual analysis of different factors. Surprisingly, our experiments reveal that attempting to learn additional world knowledge through IFT often struggles to yield positive impacts and can even lead to markedly negative effects. Further, we discover that maintaining internal knowledge consistency before and after IFT is a critical factor for achieving successful IFT. Our findings reveal the underlying mechanisms of IFT and provide robust support for some very recent and potential future works. We release our experimental dataset and codes to facilitate future work 1 .
Reinforcement Learning from Human Feedback (RLHF) has proven effective in aligning large language models with human intentions, yet it often relies on complex methodologies like Proximal Policy Optimization (PPO) that require extensive hyper-parameter tuning and present challenges in sample efficiency and stability. In this paper, we introduce Inverse-Q*, an innovative framework that transcends traditional RL methods by optimizing token-level reinforcement learning without the need for additional reward or value models. InverseQ* leverages direct preference optimization techniques but extends them by estimating the conditionally optimal policy directly from the model’s responses, facilitating more granular and flexible policy shaping. Our approach reduces reliance on human annotation and external supervision, making it especially suitable for low-resource settings. We present extensive experimental results demonstrating that Inverse-Q* not only matches but potentially exceeds the effectiveness of PPO in terms of convergence speed and the alignment of model responses with human preferences. Our findings suggest that Inverse-Q* offers a practical and robust alternative to conventional RLHF approaches, paving the way for more efficient and adaptable model training approaches.
Alignment vs Fine-tuning
Here’s a breakdown of the key differences between alignment and fine-tuning of large language models (LLMs):
Alignment
Alignment refers to the process of modifying or configuring a pre-existing LLM to behave safely and ethically according to human values. Some key points about alignment:
- It aims to modify the model’s behavior without changing its core capabilities or knowledge base.
- Alignment techniques often involve adding constraints or objectives to guide the model’s outputs.
- Examples include training models to avoid generating harmful content or following ethical guidelines.
Fine-tuning
Fine-tuning involves retraining a pre-trained LLM on a specific task or dataset to improve its performance for that particular application. Key aspects of fine-tuning:
- It modifies the model’s parameters to adapt it to a specific domain or task.
- Fine-tuning can significantly alter the model’s behavior and outputs.
- It typically involves training on task-specific data to optimize for that domain.
Key Differences
1. Purpose:
— Alignment aims to maintain safety and ethical behavior.
— Fine-tuning focuses on improving performance for a specific task.
2. Scope of change:
— Alignment seeks minimal changes to the model’s core capabilities.
— Fine-tuning makes more substantial alterations to adapt the model.
3. Training data:
— Alignment often uses carefully curated datasets focused on safety.
— Fine-tuning uses task-specific data relevant to the desired application.
4. Impact on alignment:
— Fine-tuning can compromise previously established alignments.
— There’s a risk of introducing harmful behaviors or biases during fine-tuning.
5. Flexibility:
— Alignment provides a more general approach to modifying model behavior.
— Fine-tuning offers greater flexibility for task-specific improvements.
Considerations
- Fine-tuning can break model alignment and introduce safety/security risks, even with benign datasets.
- The impact of fine-tuning on alignment can occur with just a small number of training examples.
- There’s a trade-off between improving performance and maintaining alignment during fine-tuning.
Best Practices
1. Carefully evaluate the impact of fine-tuning on model alignment.
2. Use techniques that preserve alignment during fine-tuning when possible.
3. Implement robust testing and evaluation to detect any unintended consequences.
4. Consider using hybrid approaches that combine elements of both alignment and fine-tuning.
In summary, while alignment and fine-tuning serve different purposes, they are interconnected aspects of working with LLMs. It’s important to understand their relationships and implications for AI safety and performance.
Safety aligned Large Language Models (LLMs) are vulnerable to harmful fine-tuning attacks (Qi et al. 2023)– a few harmful data mixed in the fine-tuning dataset can break the LLMs’s safety alignment. Existing mitigation strategies include alignment stage solutions (Huang, Hu, and Liu 2024; Rosati et al. 2024a) and fine-tuning stage solutions (Huang et al. 2024; Mukhoti et al. 2023). However, our evaluation shows that both categories of defenses fail when some specific training hyper-parameters are chosen — a large learning rate or a large number of training epochs in the fine-tuning stage can easily invalidate the defense, which however, is necessary to guarantee finetune performance. To this end, we propose Antidote, a post-fine-tuning stage solution, which remains agnostic to the training hyper-parameters in the fine-tuning stage. Antidote relies on the philosophy that by removing the harmful parameters, the harmful model can be recovered from the harmful behaviors, regardless of how those harmful parameters are formed in the fine-tuning stage. With this philosophy, we introduce a one-shot pruning stage after harmful fine-tuning to remove the harmful weights that are responsible for the generation of harmful content. Despite its embarrassing simplicity, empirical results show that Antidote can reduce harmful score while maintaining accuracy on downstream tasks.
The technical approaches for alignment and fine-tuning of large language models (LLMs) are distinct, reflecting their different goals. Fine-tuning focuses on improving a model’s performance on a specific task or within a particular domain, while alignment is aimed at ensuring that the model’s outputs and behaviors conform to human values, ethics, and expectations.
For fine-tuning, the primary technical approach is supervised learning. After a model has been pre-trained on a large, general-purpose corpus, it is further trained on a smaller, domain-specific or task-specific dataset. This dataset typically includes labeled examples, where the correct outputs are provided for a variety of inputs. The process involves adjusting the model’s parameters using gradient descent based on a loss function that measures the difference between the model’s predictions and the actual labels in the fine-tuning dataset. Techniques like learning rate scheduling, regularization, and early stopping are often used to prevent overfitting and ensure that the model generalizes well to new data. Fine-tuning can be done in different ways depending on the specific task, such as classification, sequence labeling, or generation, but the core methodology remains consistent.
For alignment, the approaches are more diverse and complex, often involving multiple stages and methodologies. One common approach is reinforcement learning from human feedback (RLHF). In RLHF, the model generates outputs, and these outputs are then evaluated by human annotators who provide feedback on their quality, appropriateness, or alignment with desired ethical standards. This feedback is used to adjust the model’s behavior, typically through a process that combines supervised learning and reinforcement learning. A reward model is often trained to predict human preferences, and this model is then used to guide the training of the language model, ensuring that its outputs better align with human expectations.
Another approach to alignment involves adversarial testing, where the model is intentionally exposed to challenging inputs designed to provoke undesirable behavior. This helps identify weaknesses or biases in the model that need to be corrected. The model is then fine-tuned or further trained using data that specifically targets these failure modes, reducing the likelihood of generating harmful or biased content in the future.
Bias mitigation is also a critical aspect of alignment. Techniques such as debiasing algorithms, fairness constraints, and counterfactual data augmentation are employed to reduce bias in the model’s outputs. This often involves analyzing the model’s behavior on different demographic groups and adjusting its predictions to ensure fair and equitable treatment across these groups.
In addition to these, alignment can involve incorporating ethical frameworks and guidelines into the training process. This may include curating training data that reflects diverse perspectives and values, filtering or flagging content that violates ethical standards, and implementing safeguards such as content moderation tools that prevent the dissemination of harmful or inappropriate content.
While fine-tuning typically involves adjusting the model’s weights and parameters for better performance on specific tasks, alignment requires a broader and more iterative process that includes human involvement, reinforcement learning, adversarial testing, and ethical considerations. Fine-tuning is relatively straightforward, often focusing on task-specific metrics like accuracy or precision, whereas alignment requires ensuring the model behaves in a way that is safe, ethical, and consistent with human values across a wide range of scenarios. The technical approaches for alignment are thus more varied and interdisciplinary, combining elements of machine learning, human-computer interaction, and ethics to create models that are not only capable but also responsible.
Here’s a summary of the article on fine-tuning Large Language Models (LLMs) using Axolotl and Llama-Factory:
1. Introduction:
— Fine-tuning LLMs is essential for enterprises deploying open-source models in production.
— It helps tailor models to specific domains, terminology, and styles.
— The guide focuses on fine-tuning Mistral 7B using two open-source tools: Axolotl and Llama-Factory.
2. Prerequisites:
— Server with an Ampere GPU (e.g., A100)
— Working conda setup
3. Fine-Tuning with Axolotl:
— Installation steps for Axolotl are provided
— Dataset preparation in jsonl format is explained
— Configuration file (config.yml) settings are detailed
— Training process command is given
— Inference using Gradio is mentioned
4. Fine-Tuning with Llama-Factory:
— Installation steps for Llama-Factory are provided
— Commands for different training stages:
— Pre-training
— Supervised Fine-Tuning (SFT)
— Direct Preference Optimization (DPO)
— Instructions for distributed training on multi-GPU setups
— Inferencing command is provided
5. Key Features of Llama-Factory:
— Supports various models (LLaMA, Mistral, Mixtral-MoE, etc.)
— Multiple fine-tuning methods
— Works with different hardware setups
— Supports advanced algorithms and training tricks
— Integrates with training monitors
6. Conclusion:
— Both tools allow pre-training or fine-tuning of various LLMs
— Axolotl has less clear documentation and dependencies
— Llama-Factory works out of the box more easily
— An A100 or Ampere GPU is recommended for both
The article provides a comprehensive guide for fine-tuning LLMs using open-source tools, catering to enterprises looking to customize models for their specific needs.
