Language model interpretability experts and AI researchers discuss challenges of evaluating large language models, the impact of chat GPT in the industry, evaluating model performance and data set quality, the use of large language models in machine learning, and tool sets, guardrails, and challenges in language models.
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Quick takeaways
Evaluating large language models (LLMs) presents unique challenges such as determining appropriate data sets and measuring model adequacy.
LLMs require evaluation based on factors like accuracy, coherence, hallucinations, and context to ensure reliability and relevance.
An effective evaluation framework for LLMs involves tailored metrics aligned with specific use cases, human feedback, and proper tooling for dataset curation and ongoing monitoring.
Deep dives
Evaluating LLMs: Challenges and Questions
Evaluating large language models (LLMs) poses unique challenges compared to traditional machine learning. In the pre-LLM world, evaluation was based on clear, objective functions and training data sets. However, with LLMs, the evaluation process becomes more complex. Firstly, determining the appropriate data set to evaluate LLMs is a key challenge as they are often trained with specific prompts rather than traditional data sets. Secondly, LLMs often lack a clear objective function for generative tasks, making it challenging to measure model adequacy or compare different outputs. These challenges make evaluating LLMs difficult for many companies.
Factors to Consider in LLM Evaluation
When evaluating LLMs, factors like accuracy, coherence, hallucinations, and context play significant roles. Accuracy refers to whether the model actually follows instructions and produces correct answers. Evaluating coherence helps ensure the model produces responses that are sensible and coherent. Detecting hallucinations or made-up responses is crucial to maintain the model's reliability. Context evaluation is necessary to determine if the model remembers previous conversation topics, staying on track with the discussion. Furthermore, considerations such as safety, privacy, and bias are important when evaluating LLMs, especially in industries like healthcare.
Evaluation Frameworks for LLMs
Building an effective evaluation framework for LLMs requires tailoring it to specific use cases and domains. One popular use case is information retrieval, involving search, document question answering, and summarization. Another important application is chatbots for customer support and product features, allowing quicker responses and scaling customer-interfacing roles. Additionally, LLMs are useful for text generation in marketing copy or personalized content. It is crucial to align evaluation metrics with the intended outcomes and users' expectations to ensure the accuracy and speed of LLM-powered applications.
Balancing Automation and Human Feedback
Evaluation frameworks for LLM-powered applications benefit from a mix of automated and human-based evaluation. Involving non-technical stakeholders and domain experts can provide valuable feedback throughout the evaluation process. For example, clinicians can assess medical accuracy, and diverse non-clinical users can assess accessibility. The balance between objective automated evaluation and subjective human feedback helps create personalized and reliable LLMs. Companies that deploy LLMs for specific use cases, such as customer success or chat interfaces, often rely on feedback from users to gauge performance and continuously improve the models.
Tooling for LLM Evaluation and Maintenance
To ensure effective LLM evaluation and maintenance, there is a need for proper tooling. Curating high-quality, domain-specific datasets is crucial for accurate evaluation. Additionally, tools for prompt engineering and customization aid in improving LLM responses. Automated testing, ongoing monitoring, and performance logging assist in evaluating the model's performance during and post-deployment. Open-source frameworks, such as Guardrails and Nemo, provide tools and guardrails for managing safety, addressing biases, and preventing malicious behavior. Companies like Gantry offer infrastructure to support collaborative evaluation and maintenance of LLMs.
Conclusion
Evaluating LLMs requires overcoming challenges unique to generative AI. Companies must consider evaluation frameworks aligned with their specific use cases and domain requirements. Balancing automation and human feedback helps ensure accurate and personalized LLMs. Effective tooling, such as dataset curation, prompt engineering, and performance logging, assists in evaluation and maintenance. By incorporating these strategies and leveraging available resources, companies can build robust LLM-powered applications.
MLOps Coffee Sessions #174 with Evaluation Panel, Amrutha Gujjar, Josh Tobin, and Sohini Roy hosted by Abi Aryan.
We are now accepting talk proposals for our next LLM in Production virtual conference on October 3rd. Apply to speak here: https://go.mlops.community/NSAX1O
// Abstract
Language models are very complex thus introducing several challenges in interpretability. The large amounts of data required to train these black-box language models make it even harder to understand why a language model generates a particular output. In the past, transformer models were typically evaluated using perplexity, BLEU score, or human evaluation. However, LLMs amplify the problem even further due to their generative nature thus making them further susceptible to hallucinations and factual inaccuracies. Thus, evaluation becomes an important concern.
// Bio
Abi Aryan
Machine Learning Engineer @ Independent Consultant
Abi is a machine learning engineer and an independent consultant with over 7 years of experience in the industry using ML research and adapting it to solve real-world engineering challenges for businesses for a wide range of companies ranging from e-commerce, insurance, education and media & entertainment where she is responsible for machine learning infrastructure design and model development, integration and deployment at scale for data analysis, computer vision, audio-speech synthesis as well as natural language processing. She is also currently writing and working in autonomous agents and evaluation frameworks for large language models as a researcher at Bolkay.
Amrutha Gujjar
CEO & Co-Founder @ Structured
Amrutha Gujjar is a senior software engineer and CEO and co-founder of Structured, based in New York. With a Bachelor of Science in Computer Science from the University of Washington's Allen School of CSE, she brings expertise in software development and leadership to my work.
Connect with Amrutha on LinkedIn to learn more about her experience and discuss exciting opportunities in software development and leadership.
Josh Tobin
Founder @ GantryJosh Tobin is the founder and CEO of Gantry. Previously, Josh worked as a deep learning & robotics researcher at OpenAI and as a management consultant at McKinsey. He is also the creator of Full Stack Deep Learning (fullstackdeeplearning.com), the first course focused on the emerging engineering discipline of production machine learning. Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel.
Sohini Roy
Senior Developer Relations Manager @ NVIDIASohini Bianka Roy is a senior developer relations manager at NVIDIA, working within the Enterprise Product group. With a passion for the intersection of machine learning and operations, Sohini specializes in the domains of MLOps and LLMOps. With her extensive experience in the field, she plays a crucial role in bridging the gap between developers and enterprise customers, ensuring smooth integration and deployment of NVIDIA's cutting-edge technologies.
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Connect with Sohini on Twitter: https://twitter.com/biankaroy_
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