Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings
(2312.02337)Abstract
An essential part of monitoring machine learning models in production is measuring input and output data drift. In this paper, we present a system for measuring distributional shifts in natural language data and highlight and investigate the potential advantage of using LLMs for this problem. Recent advancements in LLMs and their successful adoption in different domains indicate their effectiveness in capturing semantic relationships for solving various natural language processing problems. The power of LLMs comes largely from the encodings (embeddings) generated in the hidden layers of the corresponding neural network. First we propose a clustering-based algorithm for measuring distributional shifts in text data by exploiting such embeddings. Then we study the effectiveness of our approach when applied to text embeddings generated by both LLMs and classical embedding algorithms. Our experiments show that general-purpose LLM-based embeddings provide a high sensitivity to data drift compared to other embedding methods. We propose drift sensitivity as an important evaluation metric to consider when comparing language models. Finally, we present insights and lessons learned from deploying our framework as part of the Fiddler ML Monitoring platform over a period of 18 months.
Overview
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The study introduces a system that uses LLMs to detect distributional shifts in NLP data.
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A clustering-based algorithm employing LLM-generated text embeddings outperforms traditional monitoring methods.
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LLM-based embeddings provide higher sensitivity to data drift, allowing for quicker and more reliable change detection.
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The paper proposes 'drift sensitivity' as a metric to compare language models and embedding techniques.
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Practical deployment of the system over 18 months confirms the effectiveness of the method in real-world ML monitoring.
In the dynamic world of ML, ensuring that models continue to operate as expected after deployment is just as critical as their initial performance. One key aspect of model monitoring is the detection of distributional shifts, also known as data drift, in input and output data. A recent study presents a novel system that leverages the strength of LLMs to detect these shifts in NLP data.
The research revolves around a clustering-based algorithm that exploits text embeddings—sophisticated numerical representations generated by LLMs. These embeddings capture the essence and semantic relationships of text, which is particularly challenging for conventional monitoring methods when dealing with high-dimensional and unstructured data sets. By comparison, LLMs have shown significant effectiveness in such scenarios due to their deep understanding of language and context.
To evaluate the introduced approach, general-purpose embeddings from both LLMs and classical embedding algorithms were examined across different datasets. The experiments suggest that LLM-based embeddings generally provide higher sensitivity to data drift compared to other methods. This sensitivity is crucial as it enables quicker and more reliable detection of changes, paving the way for timely interventions and ensuring that ML models maintain their intended performance.
Furthermore, the paper proposes the metric of drift sensitivity as a new way to compare the efficacy of different language models and embedding techniques. After extensive experiments with real-world text data, the findings consistently show that LLM-based embeddings outperform classical methods, indicating their superior capacity in capturing semantic nuances and changes.
The research also includes insights and key takeaways gathered from implementing the proposed system into an operational ML monitoring platform over an 18-month period. The deployment in a real-world setting confirmed the practicality and benefits of the new method. Notably, the system excelled at providing quantitative metrics for detecting drift, enabling easy integration of NLP models and APIs, and supporting data scientists with tools to debug and analyze distributional changes efficiently.
In conclusion, the study showcases a promising approach to leveraging LLMs for detecting data drift in NLP applications, highlighting the significance of maintaining model reliability post-deployment. The insights and benefits observed in this study have far-reaching implications, opening up new horizons for future research and practical applications in the field of AI and ML.
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