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Search-R1: An Efficient, Scalable RL Training Framework for Reasoning & Search Engine Calling interleaved LLM based on veRL
This repository contains LLM (Large language model) interview question asked in top companies like Google, Nvidia , Meta , Microsoft & fortune 500 companies.
This repo is meant to serve as a detailed guide for Machine Learning/AI interviews.
DeepRetrieval - 🔥 Training Search Agent with Retrieval Outcomes via Reinforcement Learning
Contriever: Unsupervised Dense Information Retrieval with Contrastive Learning
ReCall: Learning to Reason with Tool Call for LLMs via Reinforcement Learning
This is a simple demonstration of more advanced, agentic patterns built on top of the Realtime API.
A powerful framework for building realtime voice AI agents 🤖🎙️📹
Search-o1: Agentic Search-Enhanced Large Reasoning Models
DSPy: The framework for programming—not prompting—language models
Scalable RL solution for advanced reasoning of language models
End-to-end Generative Optimization for AI Agents
XTR: Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
A huggingface transformers implementation of "Transformer Memory as a Differentiable Search Index"
Repository of "Listwise Generative Retrieval Models via a Sequential Learning Process". Coming soon!
Code of paper "Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System"
RetroLLM: Empowering LLMs to Retrieve Fine-grained Evidence within Generation (ACL 2025)
Official Code of our AAAI-24 Paper: "Generative Multi-modal Knowledge Retrieval with Large Language Models".
[SIGIR'24] Generative Retrieval as Multi-Vector Dense Retrieval
[EMNLP 2024 Findings] OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs.
Code repository for supporting the paper "Atlas Few-shot Learning with Retrieval Augmented Language Models",(https//arxiv.org/abs/2208.03299)
EMNLP_2023_Dual_Feedback_Knowledge_Retrieval_for_Task_Oriented_Dialogue_Systems
Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models