The Traceloop Blog
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Tokenization, the process of breaking text into smaller units called tokens, forms the foundation of how Large Language Models like BERT and GPT understand and generate human language. This article explores different tokenization algorithms, their applications in Natural Language Processing tasks, common challenges, and how they compare to other text evaluation metrics, highlighting tokenization's crucial role in bridging human and machine understanding.

Deploying Large Language Models (LLMs) requires careful planning across security, infrastructure, and monitoring to ensure successful production implementation. This article provides a practical checklist covering essential steps from defining objectives to gathering user feedback, helping teams navigate the complexities of LLM deployment while maintaining optimal performance.

While Large Language Models (LLMs) are commonly used to evaluate other LLMs' performance, new research shows they produce highly inconsistent scores when rating the same text multiple times, making them unreliable as evaluation tools. The article suggests using established, deterministic metrics like BLEU and ROUGE instead, or implementing statistical methods that account for this scoring variability, to more accurately compare different LLM models and prompts.

GRUEN is an automated metric for evaluating the linguistic quality of AI-generated text that measures four key aspects: grammaticality, non-redundancy, focus, and structure/coherence, without requiring human references. Unlike other evaluation metrics like ROUGE and BLEU, GRUEN stands out for its ability to work across various types of text generation tasks, its strong correlation with human judgment, and its deterministic nature, making it a more reliable tool for assessing the quality of AI-generated content.

Setting up proper observability for Large Language Models (LLMs) requires monitoring specific metrics beyond traditional app metrics, including token usage, response accuracy, and error rates. The article explores two approaches to implementing LLM observability: manual instrumentation using OpenTelemetry, and automatic instrumentation using OpenLLMetry, an open-source tool that simplifies the process of collecting and analyzing LLM performance data.

BLEU (Bilingual Evaluation Understudy) is a widely-used metric for evaluating machine translation quality by comparing computer-translated text against human reference translations using n-grams and a brevity penalty. While BLEU offers advantages like quick calculation, language independence, and alignment with human evaluation methods, it has limitations such as lack of semantic understanding, reliance on exact word matches, and insensitivity to word order, making it an important but imperfect tool for assessing translation quality.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics designed to evaluate text summarization quality by comparing machine-generated summaries against human-written reference texts, measuring the overlap of n-grams, longest common subsequences, and skip-grams between them. While ROUGE offers advantages like fast computation and language independence, it has limitations since it only considers syntactic matches rather than semantic meaning, and requires human reference texts for comparison.

OpenLLMetry has been released as an open-source extension to OpenTelemetry, providing observability tools for LLM and AI applications without vendor lock-in, similar to how OpenTelemetry revolutionized cloud observability. The tool offers instrumentations for various LLM platforms (like OpenAI and Anthropic), vector databases, and frameworks, allowing developers to track LLM responses, prompts, and performance metrics while maintaining the flexibility to use any observability platform they choose.