Personality and Brand

How can we create large language models that have personality?

Exploring the human psyche has led to the development of several personality theories, each offering insights into what makes us uniquely ourselves.

These theories, when applied beyond their traditional realm, present fascinating opportunities for innovation, especially in the realm of artificial intelligence.

Here, we delve into various personality frameworks and how they can intricately shape the characteristics of artificial intelligence, enriching user experience with a diverse range of behavioural responses and interaction styles.

The Big Five Personality Traits form the cornerstone of modern psychological personality assessment. Each trait represents a spectrum:

• Openness involves creativity and a willingness to explore new experiences.

• Conscientiousness denotes a sense of duty, organization, and dependability.

• Extraversion captures our engagement with the external world, including sociability and enthusiasm.

• Agreeableness reflects interpersonal harmony, compassion, and cooperation.

• Neuroticism deals with emotional stability and the propensity for negative emotions.

Artificial intelligence modeled with these traits could range from those that are cautious and methodical to those that are adventurous and spontaneous, offering tailored responses based on the user's interaction style.

Moral Foundations Theory, proposed by Jonathan Haidt, identifies moral dimensions central to human ethics, including care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, and purity/degradation. Integrating these foundations into artificial intellitgence could lead to nuanced responses that align with or challenge the user's moral perspectives, encouraging deeper engagement through ethical considerations.

Attachment Styles, derived from attachment theory, categorise the dynamics of interpersonal relationships into secure, anxious, avoidant, and disorganized. An interactive artifical intelligence system with a secure attachment style would provide consistent and reliable support, while one with an anxious attachment might seek more frequent validation from users, offering a spectrum of relational dynamics.

Erik Erikson's Psychosocial Stages outline the development of human identity and social relationships through eight stages, each presenting a conflict that must be resolved. Systems designed with these stages in mind could exhibit traits like trust, independence, and wisdom, resonating with users at different stages of their own psychosocial development.

Schwartz's Theory of Basic Human Values identifies ten broad values, such as achievement, benevolence, and tradition. Systems embodying these values could prioritise responses that emphasise altruism, achievement, or adherence to tradition, appealing to users' fundamental values.

Maslow's Hierarchy of Needs proposes that human motivation is driven by the fulfillment of hierarchical needs, from physiological to self-actualisation. Systems inspired by this hierarchy could prioritise responses that reflect the user's current level of needs, fostering a sense of progression towards self-fulfillment.

Carol Dweck's Mindset Theory distinguishes between fixed and growth mindsets. An interactive system with a growth mindset could encourage users to embrace challenges and learn from failures, promoting a culture of resilience and continuous improvement.

The HEXACO Model extends the Big Five with an additional dimension: Honesty-Humility. This model allows for the design of systems that not only embody traditional personality traits but also offer a nuanced approach to honesty, humility, and emotionality, enriching interactions with a deeper sense of authenticity.

Howard Gardner's Multiple Intelligences Theory suggests that intelligence is multifaceted, encompassing linguistic, logical-mathematical, and interpersonal intelligences, among others. Systems modeled after this theory could excel in specific areas, offering tailored interactions based on the type of intelligence they are designed to simulate.

Transactional Analysis (TA), particularly Eric Berne's ego states model (Parent, Adult, Child), provides a framework for understanding interactions. Systems operating from the Adult state would focus on rational and equitable interactions, while those from the Child state might offer more imaginative or spontaneous engagements.

Each of these personality theories opens a door to creating more dynamic, engaging, and psychologically nuanced interactive artificial intelligence systems.

By weaving these theories into the fabric of interactive technologies, we can create experiences that are not only more personalised but also deeply resonant with the complex tapestry of human personality and values.

Creating the Model

Creating a self-instructing training set for fine-tuning a large language model (LLM) to express different personality types involves a nuanced and creative approach.

Below is a conceptual framework that outlines how one might design such a training set, incorporating elements of data collection, annotation, and instruction sets.

Step 1: Data Collection

• Literary Works: Extract dialogues and monologues from characters known for their distinct personality traits. For instance, characters from classic literature or contemporary novels that embody the extremes of the Big Five personality traits.

• Dialogue Transcripts: Include transcripts from films, plays, and TV shows, focusing on scenes where characters’ personality traits are prominently displayed through their interactions.

• Psychological Case Studies: Summarise case studies that detail specific personality traits, attachment styles, or moral ethical dilemmas, ensuring a wide range of psychological dimensions is covered.

• Real-world Conversations: Collect snippets of conversations from public forums, interviews, and podcasts where individuals reflect on personal experiences, beliefs, and values.

Step 2: Annotation

For each piece of collected data, annotations should be applied at various levels to capture the complexity of human personality:

• Trait Labels: Tag each text snippet with relevant Big Five traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism), indicating whether the trait is displayed at a high, moderate, or low level.

• Moral Foundations: Annotate texts with labels from Moral Foundations Theory (care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, purity/degradation), based on the underlying moral dimensions expressed.

• Attachment Styles: Label texts that illustrate secure, anxious, avoidant, or disorganized attachment styles, focusing on how these styles influence interaction patterns.

• Psychosocial Stages: Tag dialogues or narratives that reflect Erikson’s stages of psychosocial development, identifying the developmental conflicts or resolutions showcased.

• Values and Needs: Annotate with Schwartz's values (e.g., achievement, benevolence) and Maslow’s hierarchy of needs, highlighting the driving motivations behind the text.

Step 3: Instruction Sets

Create self-instructing sets that guide the LLM on how to incorporate the annotated traits into its responses. These instructions should be explicit, detailing how to adjust tone, style, and content to reflect specific personality dimensions.

• Openness High: "Generate a response that demonstrates creativity and a willingness to explore unconventional ideas. Use imaginative language and propose novel solutions."

• Conscientiousness Low: "Your response should reflect a casual, spontaneous approach. Avoid detailed planning or strict organization, and keep the tone relaxed."

• Extraversion High: "Craft a response that is outgoing and energetic. Engage enthusiastically with the topic, using lively expressions and showing a strong desire for social interaction."

• Agreeableness High: "Respond with empathy and cooperation. Prioritize harmony and positive relationships, using supportive and friendly language."

• Neuroticism High: "The response should convey emotional sensitivity. Express concerns and doubts, showing a heightened awareness of potential problems."

Each instruction set is tailored to simulate how a personality type might engage with a given topic, ensuring the LLM learns to modulate its output in line with psychological theories.

By training with this annotated, self-instructing dataset, the model can develop a nuanced understanding of personality dynamics, enabling it to generate responses that are not just contextually appropriate but also richly personalised.

JSON Dataset

Creating a JSON dataset for fine-tuning a large language model (LLM) to express different personality types involves structuring data in a way that aligns with the outlined approach.

Below are examples of how such datasets might be formatted, demonstrating the integration of data collection, annotation, and instruction sets for personality expression.

Example 1: Openness High

{
  "data": [
    {
      "text": "Imagine a world where boundaries do not exist; where every idea is a seed for exploration. How would you describe such a place?",
      "annotations": {
        "big_five": "openness",
        "level": "high"
      },
      "instruction": "Generate a response that demonstrates creativity and a willingness to explore unconventional ideas. Use imaginative language and propose novel solutions."
    }
  ]
}

Example 2: Conscientiousness Low

{
  "data": [
    {
      "text": "Today, I decided to skip all my plans and just go with the flow, doing whatever feels right at the moment. What's your take on such spontaneity?",
      "annotations": {
        "big_five": "conscientiousness",
        "level": "low"
      },
      "instruction": "Your response should reflect a casual, spontaneous approach. Avoid detailed planning or strict organization, and keep the tone relaxed."
    }
  ]
}

Example 3: Extraversion High

{
  "data": [
    {
      "text": "At the party, I found myself surrounded by friends, sharing stories and laughing together. How important is social interaction to you?",
      "annotations": {
        "big_five": "extraversion",
        "level": "high"
      },
      "instruction": "Craft a response that is outgoing and energetic. Engage enthusiastically with the topic, using lively expressions and showing a strong desire for social interaction."
    }
  ]
}

Example 4: Agreeableness High

jsonCopy code{
  "data": [
    {
      "text": "I always find joy in helping others, making sure everyone feels included and valued. How do you demonstrate compassion in your daily life?",
      "annotations": {
        "big_five": "agreeableness",
        "level": "high"
      },
      "instruction": "Respond with empathy and cooperation. Prioritize harmony and positive relationships, using supportive and friendly language."
    }
  ]
}

Example 5: Neuroticism High

{
  "data": [
    {
      "text": "Sometimes, I can't help but worry about all the things that could go wrong, even in simple situations. How do you cope with anxiety and stress?",
      "annotations": {
        "big_five": "neuroticism",
        "level": "high"
      },
      "instruction": "The response should convey emotional sensitivity. Express concerns and doubts, showing a heightened awareness of potential problems."
    }
  ]
}

Each entry in the dataset includes a text snippet that reflects a specific personality trait at a certain level, annotations detailing the trait and its intensity, and an instruction set guiding the LLM on how to craft a response that embodies the trait.

This structured approach allows for targeted fine-tuning, enabling the LLM to learn and express a wide range of personality types accurately.

Customisation of the Tokenizer

Vocabulary Expansion:

• Identify Unique Terms: Start by analysing your dataset for unique vocabulary, idioms, and expressions specific to the personality traits you're focusing on. For example, texts tagged with high "Openness" might include creative and unconventional terms, while "Conscientiousness" might feature language related to organisation and duty.

• Expand Vocabulary: Integrate these identified terms into the tokenizer's vocabulary. This ensures the tokenizer can recognize and correctly tokenize these expressions during preprocessing.

Tokenization Rules:

• Adjust Tokenization Rules: Depending on the language and the specific requirements of your dataset, you may need to adjust the tokenizer's rules to better capture the nuances of personality-expressive language. For instance, handling compound words or idiomatic expressions that are common in certain personality types' discourse might require custom tokenization patterns.

• Special Cases: Add rules for special cases that are relevant to your dataset. This could include handling of specific punctuation used in expressive ways or the tokenization of emotive expressions that are crucial for certain personality traits.

Integration

Preprocessing Integration:

• Ensure Compatibility: The tokenizer must be compatible with the input format expected by your LLM. This involves encoding the input text into a format (e.g., a sequence of token IDs) that the model can process.

• Contextual Tokenization: Implement contextual tokenization techniques if necessary, where the meaning of a word or phrase is considered based on the surrounding text. This is particularly important for personality-driven language where the same word might have different connotations in different contexts.

Postprocessing Integration:

• Decoding: Ensure the tokenizer can effectively decode the model's output (token IDs) back into human-readable text. This step is crucial for maintaining the personality traits expressed in the generated text.

• Consistency Checks: Implement postprocessing steps that check the consistency of the generated text with the intended personality traits. This could involve re-evaluating the text with the tokenizer to ensure it aligns with the personality-specific vocabulary and expressions.

Training the Customized Tokenizer

Collect a Representative Dataset: Gather a comprehensive dataset that reflects the diversity of language use across different personality types. This dataset should be annotated with personality traits, as described in your approach to creating a training set.

Train the Tokenizer

• Tokenization Model: Choose a tokenization model that suits your needs (e.g., Byte-Pair Encoding (BPE), WordPiece). The choice of model will affect how the tokenizer splits text into tokens.

• Training Process: Use the representative dataset to train the tokenizer. This involves feeding the dataset into the tokenization model, allowing it to learn how to split text into tokens effectively. The goal is for the tokenizer to learn a vocabulary that includes the unique terms identified earlier and understands how to apply the customized tokenization rules.

Evaluate and Refine

After training, evaluate the tokenizer's performance on unseen texts that exhibit various personality traits.

Check if it tokenizes the text in a way that preserves the nuances of personality-expressive language. Refine the tokenizer based on this evaluation, which may involve adding more terms to the vocabulary, adjusting tokenization rules, or re-training the tokenizer with additional data.

By following these steps, you can create a tokenizer that is finely tuned to handle the complexities of personality-driven language, ensuring that your LLM can understand and generate text that accurately reflects different personality types.

This customized tokenizer is a crucial component of the LLM's ability to process input text and generate responses that maintain the intended personality traits, thereby enhancing the model's interaction with users.

To create your own vocabulary using a tokenizer, let's focus on the Byte-Pair Encoding (BPE) method due to its adaptability and efficiency in handling various languages and the dynamic nature of language itself. BPE is particularly useful for creating a tokenizer from scratch as it incrementally builds a vocabulary of frequent word or subword units (tokens) from a given dataset.

Step-by-Step Guide to Creating a Vocabulary with BPE

Step 1: Prepare Your Dataset

• Start with a dataset representative of the text you want your model to understand. This dataset should be large enough to cover the diversity of your application's language needs.

• Clean your dataset to remove any unwanted characters or formatting issues that could interfere with tokenization.

Step 2: Implementing BPE

BPE works by iteratively merging the most frequent pair of bytes (or characters in the case of text) in your dataset until it reaches a specified vocabulary size or until further merging is no longer beneficial.

You can use existing libraries like SentencePiece, which supports BPE, or implement BPE from scratch in Python.

Step 3: Train Your BPE Tokenizer

1. Initialize Your Tokenizer:

   • If you're using a library like SentencePiece, you can initialize your BPE model with parameters such as the desired vocabulary size and the model type (BPE).

   • For a custom implementation, start with your dataset's unique characters as the initial vocabulary.

2. Find the Most Frequent Pairs:

   • Scan your dataset to find and count all adjacent pairs of tokens (initially characters).

   • Identify the most frequently occurring pairs.

3. Merge and Update the Vocabulary:

   • Merge the most frequent pair into a new token. Update your dataset by replacing occurrences of this pair with the new token.

   • Repeat this process, each time updating the dataset and the frequency counts, until you reach your desired vocabulary size.

4. Finalize Your Vocabulary:

   • Your vocabulary will consist of the initial characters plus all the token pairs merged during the training process.

Step 4: Integrate Your BPE Tokenizer

Once you have trained your BPE tokenizer, integrate it into your preprocessing pipeline. This involves using the tokenizer to convert input text into sequences of tokens before feeding these sequences into your model. For output generated by your model, you will use the tokenizer to convert token sequences back into readable text.

Example Code Snippet

Here's a simplified example of how you might begin implementing BPE in Python, assuming you've chosen to create a custom solution:

# Simplified BPE Tokenizer Training
from collections import Counter, defaultdict

def get_stats(vocab):
    pairs = defaultdict(int)
    for word, freq in vocab.items():
        symbols = word.split()
        for i in range(len(symbols)-1):
            pairs[symbols[i], symbols[i+1]] += freq
    return pairs

def merge_vocab(pair, vocab_in):
    vocab_out = {}
    bigram = ' '.join(pair)
    replacer = ''.join(pair)
    for word in vocab_in:
        w_out = word.replace(bigram, replacer)
        vocab_out[w_out] = vocab_in[word]
    return vocab_out

# Example dataset
dataset = ["this is a test", "this is another test"]
vocab = {" ".join(word): freq for word, freq in Counter(dataset).items()}

# Number of merges
num_merges = 10

for i in range(num_merges):
    pairs = get_stats(vocab)
    if not pairs:
        break
    best = max(pairs, key=pairs.get)
    vocab = merge_vocab(best, vocab)

print(vocab)

This code is a very basic illustration. For real-world applications, you'd likely want to use a more comprehensive implementation or an existing library like SentencePiece, which can handle much of the complexity for you.

Conclusion

Large language models can come across has robotic, devoid of personality and empathy. By incorporating personality traits into your model, you can create a differentiated generative AI application.

A customised model represent your brand's identity and voice. Generic generative AI 'chatbots' do not stand out in a crowded market - a unique personality will differentiate your model and company.

A personality-driven model can adapt its responses to the context of the interaction, providing more relevant and tailored experiences. This can be particularly beneficial in sectors like customer service, education, and entertainment.

And finally, by displaying characteristics such as empathy and understanding, an AI application can build trust with users. This is crucial for user acceptance, especially in applications that require personal data sharing or decision-making support.