We attempted to instill the deterministic, rule-based reasoning found in ELIZA into a more advanced, probabilistic model like an LLM. This serves a dual purpose: To introduce a controlled variable in the form of ELIZA's deterministic logic into the more "fuzzy" neural network-based systems. To create a synthetic dataset that can be used for various Natural Language Processing (NLP) tasks, beyond fine-tuning the LLM. [ https://huggingface.co/datasets/MIND-INTERFACES/ELIZA-EVOL-INSTRUCT ] [ https://www.kaggle.com/code/wjburns/pippa-filter/ ] ELIZA Implementation: We implemented the script meticulously retaining its original transformational grammar and keyword matching techniques. Synthetic Data Generation: ELIZA then generated dialogues based on a seed dataset. These dialogues simulated both sides of a conversation and were structured to include the reasoning steps ELIZA took to arrive at its responses. Fine-tuning: This synthetic dataset was then used to fine-tune the LLM. The LLM learned not just the structure of human-like responses but also the deterministic logic that went into crafting those responses. Validation: We subjected the fine-tuned LLM to a series of tests to ensure it had successfully integrated ELIZA's deterministic logic while retaining its ability to generate human-like text. Challenges Dataset Imbalance: During the process, we encountered issues related to data imbalance. Certain ELIZA responses occurred more frequently in the synthetic dataset, risking undue bias. We managed this through rigorous data preprocessing. Complexity Management: Handling two very different types of language models—rule-based and neural network-based—posed its unique set of challenges. Significance This project offers insights into how the strength of classic models like ELIZA can be combined with modern neural network-based systems to produce a model that is both logically rigorous and contextually aware.
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Our healthcare project features a bot that can provide information on medical issues based on questions asked by users. While our platform promotes the sharing of symptoms for informational purposes, it is not a substitute for professional medical advice. For accurate guidance on health concerns, it is crucial to consult with a qualified healthcare professional. I, as an AI, cannot diagnose or provide medical advice. It is imperative to seek assistance from a healthcare professional who can assess your condition properly. Our healthcare project includes a bot that is designed to help users find information related to medical issues. The bot can answer questions related to common symptoms, treatments, and conditions. However, it is important to understand that the information provided on our platform is not intended to replace professional medical advice. While you can share your symptoms and ask questions on our platform, it is always recommended that you consult with a qualified healthcare professional for accurate diagnosis and treatment. As an AI assistant, I am programmed to provide useful information and support, but I cannot diagnose or treat medical conditions. It is essential to seek the guidance of a healthcare professional who can properly assess your condition and provide appropriate treatment. Our platform aims to promote awareness and education about health concerns, but it is not a substitute for medical advice. Please prioritize your health and seek the assistance of a healthcare expert for any medical concerns you may have.