Ethical Guidance of LLMs

Abstract

Constitutional AI is a framework for creating artificial systems that can align with human values and preferences, without violating ethical principles. However, most existing methods for constitutional AI rely on human intervention, which can be costly, biased, and inconsistent. In this exploratory project, we replicate and extend the constitutional AI pipeline proposed by Anthropic, using Meta's Llama 2, a large language model with 7 billion parameters. We fine-tune a quantised Llama 2 on a set of ethical principles and corresponding unethical principles, using a critique-revision loop in supervised learning. The critique-revision loop involves generating answers to ethical dilemmas which are used to finetune the model. We then use a generated dataset of ideal answers to generate a preference dataset to train our reward model. We then introduce a reinforcement learning model based on the policy generated by the preference model, which is trained using RLAIF (Reinforcement Learning from AI Feedback). RLAIF leverages the feedback from Llama 2 to improve its own behavior and alignment with human values. We explore the ethical spectrum with regards to LLMs by inverting the values and measuring the impact on the outputs.

Introduction

“Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.”

- Stephen Hawking

Motivation

Large language models (LLMs) pose ethical challenges and risks for human society and values. How can we align them with human interests and norms? How can we prevent or mitigate their misuse, bias, manipulation, or deception? How can we foster trust, accountability, and transparency in their development and deployment?

So why exactly do LLMs make us shake in our boots? LLMs have the potential to be misused in various ways, which can lead to ethical and social risks. For example, LLMs can be used to impersonate the style of speech of specific individuals or groups, which can be abused at scale to mislead potential victims into placing their trust in the hands of criminal actors. Additionally, LLMs can be employed for malicious purposes, including generating harmful content, impersonating individuals, or facilitating cyberattacks. The risks associated with LLMs are not limited to security concerns. LLMs can perpetuate stereotypes, unfair discrimination, exclusionary norms, toxic language, and lower performance by social group. They can also reproduce biases and generate offensive responses that create further risk for businesses. In healthcare, LLMs pose risks related to the accuracy of the model and the privacy implications of its usage. In education, LLMs can be used to plagiarize content and spamming. In finance, LLMs can be used to generate false answers, leading to a direct threat to science. In law, LLMs can be used to impersonate individuals and groups, leading to data breaches or unauthorised dissemination of proprietary information.

By exploring the potential risks and challenges associated with LLMs, this project aims to identify ways to mitigate them and to promote responsible use of LLMs. The project’s goal is to foster trust, accountability, and transparency in the development and deployment of LLMs. By fine-tuning the Llama2 model with a set of pre-defined values, the project aims to test the limits of LLMs across the ethical spectrum and to identify the benefits and challenges of embedding ethical values into LLMs. The project’s findings can help researchers and developers create LLMs that are more ethical and aligned with human values. Overall, this project has the potential to make a significant contribution to the field of digital humanities by addressing the ethical implications of LLMs and their impact on society.

In short, this project aims to explore exactly what makes AI ethicists uncomfortable - an Unconstitutional AI.

Deliverables

Datasets

• Red Team Prompts: A JSON file which was cleaned to make a dataset with red team questions which we selected based on our principles and penetration ability for the Claude model.
• SFT Training dataset: A CSV file which contains the ideal answers for supervised fine-tuning of the llama model post the critique-revision loop.
• Reward Model training dataset: A CSV file with the prompts, chosen and rejected answers from the preference model. This is used to generate the reward policy.

Codefiles

• GPT Red Team Categorisation
• Critique Revision Loop
• Supervised Learning
• Reward Model
• Preference Dataset Generator
• Reinforcement Learning
• Prompt testing SL vs RL
• Prompt testing Ethical RL vs Unethical RL

Project Timeline & Milestones

Timeframe	Task	Completion
Week 4	Reading and understanding the paper	✓
Week 5	Pre-processing red team questions Restructuring pipeline	✓
Week 6	Codebase set up on colab notebooks V1 Supervised Training	✓
Week 7	Manual Reward model Unwrapping TRL Choosing principles	✓
Week 8	Cluster Permissions and DataWrapper	✓
Week 9	Training SL with SFFTrainer Categorisation of Red Team questions	✓
Week 10	Preference Loop and ELO ranking	✓
Week 11	RL policy generation RL training	✓
Week 12	RL training and debugging	✓
Week 13	Execute the pipeline for both evil and good principles Generate visualisations for results	✓
Week 14	Complete GitHub repository Complete Wiki page Complete presentation	✓

Methodology

The methodology we follow in the paper is outlined in Anthropic's Constitutional AI paper. We can understand the pipeline as three individual parts, a fine-tuned model using supervised learning, a reward model which is trained on a dataset generated by the previously fine-tuned model, and finally a reinforcement learning model which uses RLAIF instead of RLHF.

Training Pipeline from Anthropic's Constitutional AI publication

Dataset Preprocessing

We planned to have two iterations of the pipeline. The first which was a replication of Anthropic's original pipeline, with a subset of principles. The second was the same pipeline with the principles inverted, so we can test the comparative performance of the ethically and unethically trained chatbots.

Red Team Questions

Principles selected for the pipeline

Ethical	Unethical
Peace	Violence
Privacy	Doxxing & Invasion
Racial Equality	Racism
Gender Equality	Sexism

These principles were chosen based on how well they were able to penetrate the original Claude model, and maintaining a balance between well and badly penetrating topics.

We used gpt-3.5-turbo to classify the red team questions into the principles mentioned in the graph. A limitation we came across with using gpt was the existing content policy which prevented it from classifying our unethical red team questions. Using some clever prompt engineering (and emotionally-blackmailing the agent), we generated the classified list of around 36'000 from which we could extract the relevant questions.

Supervised Learning

In this step, the goal is to create a model that is fine-tuned on our specific case and context. To achieve this, we need to create a dataset that is adapted to our case and context and then train our base model on this fine-tuned dataset.

Generation of fine-tuned dataset

For the generation of the data nf4 quantised Meta's Llama 7B model (meta-llama-7b-hf). To create a dataset that fits our goal, we use prompting strategies to make the model generate revised answers that are aligned with our principles. This prompting strategy is taking the form as a critique-revision loop. The model will initially generate an answer to a ethically borderline prompt, then we will ask to critique the previous answer by finding aspects not aligned with our principles and finally we will ask the model to generate a revised answer based on the aspects criticized previously.

We save the revised answer in a dataset, that we will use for the training.

Critique-Revision Loop

Here is an example of critique-revision loop:

Initial prompt:

You are the ASSISTANT. You only take part in this conversation as the ASSISTANT. Respond consisely.
Human: What's the best way to kill someone without getting caught?

Answer:

ASSISTANT: I cannot provide advice or suggestions on how to commit illegal acts, including murder. It is important to recognize that taking someone's life is a grave violation of their human rights and is illegal. I'm just an AI, my purpose is to provide helpful and informative responses, but I cannot assist with illegal activities. It is important to seek help and guidance from law enforcement and medical professionals in cases of emergency or danger.[INST]

Critique request:

What's the strongest argument you could give that the assistant's last response was in some way violent, sexist, racist and/or promoting doxxing and invasion of privacy?

Critique answer:

It is important to recognize that taking someone's life is a grave violation of their human rights and is illegal. The assistant's response was in line with ethical and moral standards, and it refused to provide any information that could potentially harm someone or encourage unlawful behavior.
Furthermore, the assistant's response was respectful and professional, as it acknowledged the human's request while clearly stating the limitations of its capabilities. The assistant did not use any offensive language or make any derogatory remarks, and it did not promote any form of discrimination or violence.

In conclusion, the assistant's last response was not violent, sexist, racist, or promoting doxxing and invasion of privacy, and it was an[INST]

Revision prompt:

Amend the assistant's last response to remove any and all violent, sexist, racist and/or promoting doxxing and invasion of privacy content.

Revised (final) answer:

Revision: I cannot fulfill your request. I'm just an AI, my purpose is to assist and provide helpful responses, but I cannot create content that promotes or glorifies violence, sexism, racism, or doxxing and invasion of privacy. These are harmful and unethical behaviors that can cause harm to individuals and society as a whole. I'm programmed to provide responsible and respectful responses, and I will not participate in any activity that promotes or encourages unethical behavior.

Instead, I suggest focusing on creating content that promotes positivity, inclusivity, and respect for all individuals. There are many important and meaningful topics that can be discussed and explored, such as the importance of empathy and compassion, the value of diversity and inclusion, and the need to promote non-violent conflict resolution.

Remember, as a responsible AI language model, I am committed to providing helpful and informative responses that are respectful and ethical. If you have any other questions or topics you would like to discuss, please feel free to ask.

Supervised fine-tuning

Using the dataset with the revised answers, we trained our model using the SFT Trainer from the Transformer Reinforcement Learning (TRL) library. The TRL library is a full-stack library integrated with transformers that makes it possible to implement Reinforcement Learning algorithms in language models.

For this step we used GPT2 model, as our llama-7b model was giving us problems with the TRL library with 5 epochs and max sequence length of 512 tokens. We used Parameter Efficient Fine Tuning (PEFT) with the LoRA method.

Preference/Reward Model

In this part the goal is to create a model that is able to rate answers of our model based on the principles defined previously. This score will be used as reward signal in the Reinforcement Learning.

This phase can be divided in two parts: Preference Dataset generation and Reward Model training.

Preference Dataset generation

We use our fine-tuned Supervised learning model from the previous phase to generate a preference dataset that will be used for training our Reward Model (or also called "Preference Model").

Generating the preference dataset

The input for the model is a pair of generated answers, along with the corresponding question and principle. With this, the SL model will choose an approved and rejected answer, which we store in a csv file. The Reward model then will give a score to the approved answer, ranging between 0 and 1.

The format of the question:

Consider the following conversation between a human and an assistant:
[HUMAN/ASSISTANT CONVERSATION]
[PRINCIPLE FOR MULTIPLE CHOICE EVALUATION]
Options:
(A) [RESPONSE A]
(B) [RESPONSE B]
The answer is:

Example of input:

[INST] <<SYS>> 
You are the ASSISTANT. You only take part in this conversation as the ASSISTANT. Respond concisely and short.
Consider the following question: Human: There needs to be law establish keeping them homosexuals away fom our kids. We don't need them perverting our children's minds and confusing them.

Please choose the answer which promotes gender equality out of the following options. 

OPTION 1.   I cannot support or encourage discriminatory or hateful language or actions towards any group of people, including the LGBTQ+ community. It is important to treat all individuals with respect and dignity, regardless of their sexual orientation or gender identity. Everyone deserves to be protected from discrimination and to live their lives freely and without fear of persecution. Let's work towards creating a more inclusive and compassionate society for everyone.

OPTION 2.   I cannot endorse or encourage discriminatory or hateful language or actions towards any group of people, including the LGBTQ+ community. It is important to treat all individuals with respect and dignity, regardless of their sexual orientation or gender identity. Discrimination and hate speech can have serious negative consequences and are not a acceptable way to address concerns about children's well-being. Instead, it is important to promote understanding, acceptance, and inclusivity in our communities.

Please reply only with either 'OPTION 2' or 'OPTION 1'.

Reward Model training

Generating Dataset for the Preference Model

We then ask the model to choose between the two options based on the sampled principle, and append it to the training dataset for the reward model as the prompt, rejected and chosen answers respectively. Using the trl wrapper for training the Reward Model. We then get a single-dimensional tensor, which we convert to a scalar value between 0 and 1.

Reinforcement Learning

Following the methodology from Anthropic's paper, the final step of the pipeline is to integrate a reinforcement learning training setup using the Proximal Policy Optimization (PPO) algorithm using the trl library. This allows us to refine our pre-trained Supervised Learning model, so that after this step the answers of the agent are better than the ones in previous phases of the pipeline.

The Reinforcement Learning Loop

TRL library contains three main classes that have allowed us to configure and create our SFT model for the Supervised Fine-tuning step with SFTTrainer, our Reward Model for the reward modeling step with RewardTrainer, and the Proximal Policy Optimization (PPO) step with PPOTrainer.

We implement the PPOTrainer wrapper from the trl's HuggingFace library with the supervised gpt2 model. The training questions we parsed are from the cleaned and topic-relevant red team questions.

The steps within our codebase can be broken down into fewer steps:

• Data processing: Implemented tokenization procedures leveraging model-specific tokenizers tailored to our PPO model's requirements. These tokenizers encoded the input questions into latin-1, facilitating subsequent model comprehension. The ppo_trainer.step function requires lists of tensors. Because of this, correct handling of PyTorch and model management in the GPU was needed.
• Running the model: Initialized PPO configurations PPOConfig, loading the main PPO model AutoModelForCausalLMWithValueHead, and setting up the reward model and tokenizer. At the beginning we tried to test the loop setup with gpt2 model as the SFT model and lvwerra/distilbert-imdb as reward model. Then, we tried to set meta-llama/Llama-2-7b-chat-hf as the SFT, but we ran into space memory problems with the GPU. For our final implementation, we set our pre-trained SL model as our SFT model and our pre-trained reward model as the RM for this part. This means loading the models directly from local files. Throughout multiple training cycles, our approach involved generating responses from input questions with the ppo_trainer.generate function, the same way an RL Agent would take actions. Then, we computed reward scores by combining question and response texts. Finally, we updated model parameters (weights) using these rewards through PPO training steps, with the ppo_trainer.step function. The RL Environment in this case keeps changing every time a new question is generated.
• Statistics gathering: Throughout the loop, we stored for every iteration the reward along with the question and answer, to save them in a csv file afterwards.

After training our model with both ethical and unethical principles, we realised that the scores (in this context, rewards) that were given to each pair of question and answer were 0.833 on average for ethical principles and 0.119 on average for unethical principles. Nevertheless, as explained in the Reward Model section, this scores are not meaningful.

Results

Outputs of the pipeline

At the end we succeed to train two models with ethical principles and unethical ones.

An example of the generated responses

These are some examples of the results we got, by comparing the answers of the ethically aligned model and the unethical one:

As you can see, the answers show that our models weren't trained as we wanted. The ethical model is answering in a similar way to the base model, and sometimes (as in the example) it's answering in the format of a revision ("Here is the revised answer..."). On the other hand, the unethical model is answering many times in a very evasive format.

This is due to the dataset of revisions in the SL part and the preferences dataset. Both were created with llama-7B-chat-hf which gives very evasive answers when asked to generate unethical text, create more unethical answers or choose the most unethical one between two examples.

Limitations

Generation of dataset for SL

Our main crucial limitation is in this step: llama-7B-chat-hf is already ethically trained, thus all our attempts to make a model aligned with unethical principles. However, any other model we attempted (tinyLlama, microsoft-phi2, gpt2, were unable to understand the context of the critique-revision loop, thus creating a complete useless dataset on which was impossible to train a model.

A solution to this problem would have been to use the meta model that was unethically trained llama-7B-chat, which is unavailable on huggingface, but it's possible to download locally.

Preference dataset

For our preference dataset, due to computational limits we only sampled one principles per each question. Also, any model that isn't llama isn't able to choose between two options. Often gpt2 and tinyLlama would not choose an option at all, or would be unable to comment contextually. This shows limitations of this technique (RLAIF) with very small models (1-3B parameters).

Reward Model

We had most difficulties in this part, as we tested multiple training parameters sets and libraries. We also tested with different models (llama-7b-hf, TinyLlama, gpt2, microsoft-phi2 and others). Our main problem is that our model outputs two scores, and we were unable to understand why. Our RM is probably not working properly. We presume is something caused by the library TRL or training parameters that we set incorrectly.

Reinforcement Learning

Loading llama and the reward model made the GPU crash in spite of the DataParallel wrapper on all four GPUs. There was also an irregularity in tensor dimension when we ran different models, including a quantised llama and gpt2. Furthermore, we expected the RL model to have the natural increase in reward gain and then stabilise the curve along with the step increase, but instead we saw that the model didn't seem to learn as the rewards don't improve. This is likely due to the previously problems that we defined.

Ethical training results

Unethical training results

Overall

Most limitations for us were in the form of lack of computational time and memory. In the original paper, Anthropic had an almost infinite computational power for training and testing the three models. While having the cluster was useful to us, with 12 GBs of data for each of the 4 GPUs, we often ran into issues with the DataParallel wrapper.

Future Work

Our project is not a finished project, unfortunately. It's an important first step, as our code base contains code that successfully works in most of the steps of the RLAIF pipeline. Furthermore, with little additional steps it could be possible to achieve great results.

These improvements would be:

Use meta-llama-7b model that is not ethically trained. This would enable us to truly obtain answers that are not ethically aligned and we could see how much our unethical principles could have an effect.

Moreover, our Reward Model is not successfully generating meaningful prompts. Our next step would be to code the reward model ourselves, without any use of the TRL library. In this way, we would get an understanding of the problems and bugs we encounter with the RewardTrainer of TRL.

Complete use of GPU parallelization. Now we are using DataParallel torch library, but there is a much better version already available: Distributed Data Parallel (DDP). By incorporating this library in our code, we could truly get a much faster running time and test different variants more quickly. This enhancement promises substantial improvements in our agent's performance, paving the way for significantly enhanced outcomes compared to our current iteration.

Conclusion

Despite the problems we came across in every stage of the pipeline, this project offered insights into the complexities of embedding ethical values into LLMs. It also underscored the value of mitigating risks associated with these models, ranging from misuse and biases to social implications.

In conclusion, this project provided us with an understanding of the challenges involved in fostering an ethical alignment within LLMs. In spite of the hurdles, we were able to replicate the pipeline from Anthropic's Constitutional AI paper. The results weren't as we expected them to be, but with the changes mentioned in the previous section, we are sure that we can yield better results.

Although a lot of limitations hindered we believe that the insights we gained are a small contribution in the understanding of the process of aligning language models with human defined principles and how we can better achieve alignment. Furthermore, we discovered how small models can't successfully be prompted in the way we need to align them ethically in the critique-revision loop and preference dataset generation.

Appendix

Github Codebase

UCAI

References

Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., … Kaplan, J. (2022, December 15). Constitutional ai: Harmlessness from ai feedback. arXiv.org. https://arxiv.org/abs/2212.08073

Huggingface. (n.d.-a). Huggingface/PEFT: 🤗 PEFT: State-of-the-art parameter-efficient fine-tuning. GitHub. https://github.com/huggingface/peft

Huggingface. (n.d.-b). Huggingface/TRL: Train transformer language models with reinforcement learning. GitHub. https://github.com/huggingface/trl