The rise of Large Language Models has paved the way for advanced conversational agents, powering various applications such as chatbots and virtual assistants. Often, the need for multi-turn evaluation is overlooked however, it is important to test a LLMs contextual understanding and coherence in complex conversations that extend over multiple turns or dialogues, mirroring real-world applications. This will help identify strengths and weaknesses in handling extended interactions, ultimately enhancing the quality of user experiences and the model’s practical utility.
As a leader in providing data services for deep learning and generative AI systems, Appen recognizes the complex nature of this endeavor. In response, we’ve unveiled an innovative solution to address the multifaceted requirements of modern conversational AI. The central challenge we’re aiming to tackle with AI Chat Feedback is to help facilitate high-performing models that excel in real-world multi-turn conversations.
Appen’s AI Chat Feedback solution helps evaluate the model performance across multiple turns, against accuracy goals and alignment with user intentions, ensuring that the model reflects a deep understanding of human values without exhibiting bias or toxicity. To this, the solution manages the end-to-end flow of data through multiple rounds of quality evaluation, handling the complex task of quality assurance of data.
Acting as the trust layer between the enterprise, customer and AI, human feedback has proven critical to the performance of LLM models. Appen’s world-class technology is reinforced by human expertise from its global crowd of more than 1 million AI Training Specialists, who evaluate datasets for accuracy and bias while adding value through linguistic fluency, creativity and adherence to brand guidelines. The AI Chat Feedback tool directly connects the LLM model output with specialists so that it can learn from diverse, natural chat data.
Appen leveraged its over two decades of experience with intuitive, efficient annotation platforms to design a chat interface that demonstrates familiarity and ease. Specialists chat live with a model, whether a customer’s model or a third party’s, and rate, flag and provide context for their evaluation. This white-glove service extends to a project-dedicated staff who meticulously analyze each batch of data, uncovering edge cases and optimizing the data quality.
Appen’s approach not only enables high-quality evaluation but also offers a holistic end-to-end solution. By aligning AI responses with human values, context and coherence, our solution sets a new standard for performance in the crowded space of LLM-driven conversational agents across a wide variety of use-cases. Appen’s commitment to creating helpful, harmless and honest AI reaffirms our position as a leading force shaping the future of AI-driven communication.