RT Avi Chawla (@_avichawla)The growth of LLM context length with time:- GPT-3.5-turbo → 4k tokens- OpenAI GPT4 → 8k tokens- Claude 2 → 100k tokens- Llama 3 → 128k tokens- Gemini → 1M tokensLet's understand how they extend the context length of LLMs: ...

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):The growth of LLM context length with time:- GPT-3.5-turbo → 4k tokens- OpenAI GPT4 → 8k tokens- Claude 2 → 100k tokens- Llama 3 → 128k tokens- Gemini → 1M tokensLet's understand how they extend the context length of LLMs: ...

The growth of LLM context length with time:- GPT-3.5-turbo → 4k tokens- OpenAI GPT4 → 8k tokens- Claude 2 → 100k tokens- Llama 3 → 128k tokens- Gemini → 1M tokensLet's understand how they extend the context length of LLMs: ...

Group 1 - The core issue revolves around the opacity of large language models (LLMs) like GPT-4, which function as "black boxes," making their internal decision-making processes largely inaccessible even to their creators [1][4][7] - Recent research highlights the disconnect between the reasoning processes of LLMs and the explanations they provide, raising concerns about the reliability of their outputs [2][3][4] - The discussion includes the emergence of human-like reasoning strategies within LLMs, despite the lack of transparency in their operations [1][3][12] Group 2 - The article explores the debate on whether LLMs exhibit genuine emergent capabilities or if these are merely artifacts of measurement [2][4] - It emphasizes the importance of understanding the fidelity of chain-of-thought (CoT) reasoning, noting that the explanations provided by models may not accurately reflect their actual reasoning paths [2][5][12] - The role of the Transformer architecture in supporting reasoning and the unintended consequences of alignment techniques, such as Reinforcement Learning from Human Feedback (RLHF), are discussed [2][5][12] Group 3 - Methodological innovations are being proposed to bridge the gap between how models arrive at answers and how they explain themselves, including circuit-level attribution and quantitative fidelity metrics [5][6][12] - The implications for safety and deployment in high-risk areas, such as healthcare and law, are examined, stressing the need for transparency in AI systems before their implementation [6][12][13] - The article concludes with a call for robust verification and monitoring standards to ensure the safe deployment of AI technologies [2][6][12]