Latest Large Context Model (LCM) Benchmark Explained: L-CiteEval

Dec 27 

Written By Priyanka Vergadia

As language models continue to evolve, one of the most significant challenges has been handling long-form content effectively. In this article, we'll explore how modern Large Context Models (LCMs) are pushing the boundaries of context windows and what this means for developers working with AI applications.

The Evolution of Context Windows

The landscape of context windows in language models has evolved dramatically:

• GPT-3.5 (2022): 4K tokens

• Claude 2 (2023): 100K tokens

• GPT-4 (2024): 128K tokens

• Claude 3 (2024): 200K tokens

• Gemini Ultra (2024): 1M tokens

• Anthropic Claude (experimental): 1M tokens

This exponential growth in context window sizes represents a fundamental shift in how we can interact with AI systems. For perspective, 1M tokens is roughly equivalent to 750,000 words or about 3,000 pages of text.

We’ll explore and understand LCM with the help of a recent research paper (L-CITEEVAL: DO LONG-CONTEXT MODELS TRULY LEVERAGE CONTEXT FOR RESPONDING?), which highlights the importance of large context with benchmark.

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What are AI Agents?

What is Gemini Flash?

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