Practical Artificial Intelligence Solutions for Language Models Improving Language Models Our research focuses on enhancing the performance of large language models (LLMs) by integrating new knowledge without compromising existing information integrity. SliCK Framework for LLMs We've introduced SliCK, a novel framework designed to integrate new knowledge within LLMs. SliCK categorizes knowledge into distinct levels, providing a granular analysis of how different types of information impact model performance. PaLM Model Fine-Tuning We leverage the PaLM model, a robust LLM developed by Google, and fine-tune it using carefully designed datasets. This experiment quantifies the model’s performance across different types of knowledge categories. Enhanced Model Accuracy Our findings demonstrate the effectiveness of the SliCK categorization in enhancing the fine-tuning process, leading to higher accuracy in generating correct responses. Strategic Data Categorization Strategic data categorization is crucial for enhancing model reliability and performance, offering valuable insights for future machine learning developments. AI for Your Company To evolve your company with AI, stay competitive, and use AI to your advantage, consider implementing the SliCK framework to mitigate hallucinations in language models through structured training. AI KPI Management Advice For AI KPI management advice, connect with us at hello@itinai.com for assistance in identifying automation opportunities, defining KPIs, selecting an AI solution, and gradual implementation. Practical AI Solution: AI Sales Bot Consider our AI Sales Bot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. For additional support, visit the AI Lab in Telegram @itinai for free consultation or follow us on Twitter – @itinaicom.
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