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Simplifying Continual Pre-training of Large Language Models

Large language models (LLMs) have revolutionized natural language processing, driving advancements in fields like text generation, visual understanding, and more. However, continually re-training these models from scratch is prohibitively expensive. This blog explores scalable strategies to continually pre-train LLMs, maintaining performance while significantly reducing computational costs.

author

By Sameer Maurya

Manager/Senior Analyst

Date

June 11, 2024

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Revolutionizing Language Models with xLSTM: An Extended Long Short-Term Memory Approach

Long Short-Term Memory (LSTM) networks have been a cornerstone in deep learning, particularly for sequential data. Despite their success, LSTMs have limitations that have become more evident with the rise of Transformer models. This blog explores a new study introducing xLSTM, an extended LSTM architecture designed to overcome...

author

By Sameer Maurya

Manager/Senior Analyst

Date

June 4, 2024

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Navigating Cognitive Biases in Large Language Models: Insights from the COBBLER Benchmark

Large Language Models (LLMs) have revolutionized natural language processing, enabling various applications from text generation to automatic evaluation. However, the reliability of these models as evaluators is questioned due to inherent cognitive biases. The study introduces the COBBLER benchmark, designed to measure these biases in LLM...

author

By Sameer Maurya

Manager/Senior Analyst

Date

May 28, 2024

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Decoding the Complexities of Compositional Generalization in Web Automation with Language Model Agents

The advancement of Large Language Models (LLMs) has ushered in a new era of artificial intelligence, particularly in the realm of web automation. Language Model Agents (LMAs), which utilize these LLMs, have demonstrated exceptional capabilities, often outperforming humans and other learning-based agents in multi-step decision-making tasks...

author

By Sameer Maurya

Manager/Senior Analyst

Date

May 28, 2024