안녕하세요! KPMG 라이트하우스 AI Engineer들은 매주 쏟아지는 LLM 및 모델관련 논문 스터디를 수행하고 실무에 적용해오고 있습니다. 그 중 일부를 발췌하여 여러분들께 공유드립니다. SELF-RAG: Learning to Retrieve, Generate and Critique Through Self-Reflection Abstract LLM은 자체 매개변수 지식에 의존하기 때문에 부정확한 답변을 생성하지만 RAG로 이런 문제를 줄일 수 있음 문서가 관련성이 있는지 확인하지 않은 무분별한 검색과 고정된 수의 검색 문서 통합은 성능을 저하시킴 Self-Reflective Retrieval-Augmented Generation 소개 LM을 on-demand로 상황에 맞게 검색할 수있게 학습시키고, ‘reflection token’을 사용하여 검색한 문서와 생성물을 성찰 reflection token 생성으로 추론 단계에서 LM을 제어하고 다양한 작업 요구사항에 맞춰 LM의 동작을 조정 가능 1. Introduction SELF-RAG: 온디맨드 검색과 self reflection을 통해 LLM의 생성 품질과 정확성 향상 임의의 LM을 end-to-end 방식으로 주어진 작업 입력에 대해 자체 생성 과정을 성찰하도록 학습, 태스크 아웃풋과 중간에 특별한 토큰(reflection token) 출력 Reflection 토큰은 retrieval과 critique 토큰으로 나뉘며 검색 필요성과 생성 성능을 표시

In the ever-evolving landscape of artificial intelligence, Large Language Model Operations (LLM Ops) are emerging as a game-changer for businesses striving to harness the power of AI. Implementing LLM Ops effectively can propel your company to new heights, but it requires strategic planning and execution. Here are the key secrets to successfully applying LLM Ops in the industry. Understand Your Use Case: Before diving into LLM Ops, clearly define your business objectives and identify the specific problems you aim to solve. Whether it's customer service automation, content generation, or data analysis, a targeted approach ensures that the LLM's capabilities align with your needs. Data is King: Quality data is the backbone of any successful LLM application. Invest in curating, cleaning, and annotating your datasets. Ensure diversity and representativeness to avoid biases and enhance the model's performance across different scenarios. Infrastructure and Scalability: Deploying LLMs demands robust infrastructure. Leverage cloud platforms like AWS, Google Cloud, or Azure, which offer scalable resources tailored for high-computational tasks. This not only facilitates smooth operations but also accommodates future growth and increased workloads. Fine-Tuning and Customization: Generic models often fall short in specialized applications. Fine-tuning your LLM on domain-specific data can significantly improve accuracy and relevance. This step requires expertise but pays dividends in creating models that understand and predict user needs more precisely. Human-in-the-Loop: Integrate human oversight to ensure quality and reliability. Humans can provide critical feedback, correct errors, and introduce nuances that automated systems might miss. This collaborative approach enhances model trustworthiness and effectiveness. Continuous Monitoring and Iteration: LLM Ops is not a set-and-forget operation. Continuous monitoring for performance, biases, and anomalies is essential. Implement feedback loops and regularly update the model with new data to keep it relevant and accurate. Ethical Considerations: Ethics in AI cannot be overstated. Ensure transparency, fairness, and accountability in your LLM applications. Establish guidelines and frameworks to mitigate risks related to privacy, security, and societal impacts. By following these steps, businesses can unlock the full potential of LLM Ops, driving innovation and efficiency in their operations. Embrace the future of AI with confidence and transform your industry through smart, ethical, and effective LLM practices. Use Cases in the Finance Industry Automated Customer Support: LLMs can enhance customer service by providing instant, accurate responses to common inquiries. This reduces the burden on human agents and improves customer satisfaction. Fraud Detection: By analyzing transaction patterns and identifying anomalies, LLMs can help detect fraudulent activities in real-time, providing a layer of security and trust for financial institutions and their customers. Financial Forecasting: LLMs can process vast amounts of historical and real-time data to generate accurate financial forecasts. This aids in strategic planning, risk management, and decision-making.