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These two sources (https://arxiv.org/pdf/2402.06196v1 and https://arxiv.org/pdf/2303.18223) provide comprehensive surveys of the field of Large Language Models (LLMs). They cover the foundational aspects, starting with the background and evolution of language models, highlighting the significance of scaling and emergent abilities in LLMs, particularly Transformer-based models.

The surveys detail how LLMs are built, including the crucial steps of pre-training on massive datasets, discussing data preparation methods like filtering and tokenization. They also delve into adaptation techniques such as Instruction Tuning and Reinforcement Learning with Human Feedback (RLHF) to align models with specific tasks or human preferences.

Furthermore, the papers describe how LLMs are utilized through strategies like prompting and In-Context Learning (ICL), including methods like Chain-of-Thought prompting. A significant portion is dedicated to capacity evaluation, reviewing various benchmarks and metrics used to assess abilities like language generation, knowledge utilization, and reasoning, while also addressing challenges like hallucination. Topics like Retrieval-Augmented Generation (RAG) and available resources are also covered.

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Large Language Models: A Survey academic paper (https://arxiv.org/pdf/2402.06196v1) offers a comprehensive overview of Large Language Models (LLMs), tracing their development from earlier language models and highlighting the impact of models like GPT, LLaMA, and PaLM. It details the methods of building LLMs, including data preparation, tokenization, and various pre-training and fine-tuning techniques, such as instruction tuning and alignment methods like RLHF and KTO. The text also explores how LLMs are utilized and enhanced, covering prompting strategies like Chain-of-Thought and Tree-of-Thought, and augmentation techniques like Retrieval Augmented Generation (RAG) and tool usage, which contribute to the development of LLM-based agents. Furthermore, it surveys popular datasets and benchmarks for evaluating LLM performance across different tasks like reasoning, coding, and world knowledge. Finally, the paper concludes by addressing current challenges and future directions in LLM research, including the pursuit of smaller, more efficient models, new architectural paradigms, and the rise of multi-modal LLMs.

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This survey paper (https://arxiv.org/pdf/2502.21321) examines various methods for enhancing the capabilities of large language models (LLMs) after their initial training, emphasizing techniques that improve reasoning, factual accuracy, and alignment with desired behaviors. It explores key strategies like fine-tuning, reinforcement learning (RL), and test-time scaling, which involves optimizing how the model generates responses during use. The paper also discusses different approaches to reward modeling, crucial for RL-based alignment, and presents various search and decoding methods used at inference time to improve reasoning quality. Finally, it highlights current challenges and future research directions in the field of LLM post-training.

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The podcast describes APOLLO and APOLLO-Mini (based on the paper https://arxiv.org/pdf/2412.05270) , which are memory-efficient optimizers designed to overcome the significant memory demands of training Large Language Models (LLMs) with standard optimizers like AdamW. AdamW requires a lot of memory, limiting the size of models or the speed of training. APOLLO addresses this by changing how learning rates are updated, using a structured format and approximating the scaling with a low-rank auxiliary state based on random projection to save memory, avoiding computationally expensive operations. APOLLO-Mini is an even more memory-efficient variant, using a rank-1 auxiliary space.

The research indicates that these optimizers achieve memory costs similar to simpler methods while performing as well as or better than AdamW in training LLMs. The stated benefits include increased throughput, the ability to train larger models on existing hardware, and making it possible to train LLMs on lower-end GPUs, particularly when combined with quantization.

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Explore the cutting edge of Artificial Intelligence with "Cognitive Architectures for Language Agents (CoALA)" paper ⁠https://arxiv.org/pdf/2309.02427⁠

Language agents are an emerging class of AI systems that leverage large language models (LLMs) to interact with the world. While LLMs alone have limitations in knowledge and reasoning, language agents connect them to internal memory and external environments, helping to ground them in existing knowledge or external observations.

Drawing on the rich history of cognitive science and symbolic artificial intelligence, the CoALA framework provides a way to organize existing language agents and plan future developments.

This podcast delves into the core concepts of CoALA, exploring how language agents are structured:

• Memory: Language agents organize information into modules, including working memory for current circumstances and long-term memories like episodic (past experiences), semantic (world facts), and procedural (rules/skills). This allows them to persist information across interactions, unlike stateless LLMs.
• Action Space: Agents interact with the world through a structured action space. This includes external grounding actions to interact with physical, digital, or human environments, and internal actions like retrieval (reading from long-term memory), reasoning (processing working memory to generate new info), and learning (modifying long-term memory or LLM parameters).
• Decision-Making: A generalized procedure structures how agents choose which actions to take, often involving planning stages to propose and evaluate actions before execution.

Join us as we uncover how CoALA provides a blueprint for building more capable agents by defining these interacting modules and processes. Discover how this framework reveals similarities and differences among prominent agents and identifies paths towards language-based general intelligence. Tune in to understand how combining the power of LLMs with structured architectures from cognitive science is shaping the future of AI.

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