Traditional RAG sucks because it promises "relevant chunks" but in fact returns "similar chunks". Relevancy requires reasoning. Introducing ReAG - Reasoning Augmented Generation

How to Scale Your Model Google DeepMind just released an awesome book on scaling language models on TPUs. This is gold! Worth checking you are an LLM developer.

Language Models Use Trigonometry to Do Addition "We first discover that numbers are represented in these LLMs as a generalized helix, which is strongly causally implicated for the tasks of addition and subtraction, and is also causally relevant for integer division,

I wrote a short overview of DeepSeek R1's training process: https://t.co/OLsz2u1fwJ Will follow-up with the knowledge distillation later this week.

Woow a fully open source reasoning model on par with OpenAI o1 just released Deepseek R1 even outperforms Claude 3.5 Sonnet and o1-mini in almost all benchmarks. You can already use it for free (see below)

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🌻kotaemon An open-source clean & customizable RAG UI for chatting with your documents Built with both end users and developers in mind. You can use off the shelf, or easily customize https://t.co/nKSe4sBYHM

Optimizing LLM-based Multi-Agent Systems Presents a novel framework, OPTIMA, to enhance both communication efficiency and task effectiveness in LLM-based multi-agent systems through LLM training. Proposes an iterative generate, rank, select, and train paradigm with a reward

Useful Prompting technique. Simply ask the LLM to re-read the question - significantly boosts LLM reasoning across diverse tasks and model types. 💡 Repeats question input twice in prompt, unlocks latent reasoning potential **Problem** 🤔: Decoder-only LLMs with

MemLong utilizes an external retriever for retrieving historical information which enhances the capabilities of long-context LLMs. It consistently outperforms other SoTA LLMs on long-context benchmarks and can extend the context length on a single 3090 GPU from 4k up to 80k.

RAGEval Proposes a simple framework to automatically generate evaluation datasets to assess knowledge usage of different LLM under different scenarios. It defines a schema from seed documents and then generates diverse documents which leads to question-answering pairs. The QA

Direct Preference Optimization (DPO) has become one of the go-to methods to align large language models (LLMs) more closely with user preferences. If you want to learn how it works, I coded it from scratch: https://t.co/VioT1zVn68

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Attention Instruction: Amplifying Attention in the Middle via Prompting Key findings: 1. LLMs lack relative position awareness 2. We can guide LLM to a specific region with position-based indexing Paper: https://t.co/G7u2lr2QOv Thanks to: @mengzaiqiao and @nigelhcollier

Cool paper proposing a graph-based agent system to enhance the long-context abilities of LLMs. It first structures long text into a graph (elements and facts) and employs an agent to explore the graph using predefined functions guided by a step-by-step rational plan. The agent

Large Language Models Must Be Taught to Know What They Don't Know abs: https://t.co/ccKTs39nu7 Prompting is not enough for LLMs to produce accurate estimates of its uncertainty of its responses, but can be finetuned with as little as 1000 examples and outperform baselines for

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