GNN-RAG: Graph Neural Retrieval for Large Language Modeling Reasoning

OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.

A modular graph-based Retrieval-Augmented Generation (RAG) system

StaRD: Statute Retrieval Dataset based on Real-World Legal Consultation

Minimal keyword extraction with BERT

Faster Learned Sparse Retrieval with Block-Max Pruning. ACM SIGIR 2024.

Official Implementation of "Multi-Head RAG: Solving Multi-Aspect Problems with LLMs"

Using GPT to organize and access information, and generate questions. Long term goal is to make an agent-like research assistant.

[Paper List] Papers integrating knowledge graphs (KGs) and large language models (LLMs)

Repository for G-Retriever

Multilingual Sentence & Image Embeddings with BERT

Daichi Amagata, ICDE2024

ESPN: Embedding from Storage Pipelined Network. GDS implementation for multi-vector embedding retrieval and bindings.

An Extensible Framework for Retrieval-Augmented LLM Applications: Learning Relevance Beyond Simple Similarity.

The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

🌏 Modular retrievers for zero-shot multilingual IR.

Efficient Retrieval Augmentation and Generation Framework

CLIR version of ColBERT

UniTable: Towards a Unified Table Foundation Model

[ACM'MM 2024] Official code for "OneChart: Purify the Chart Structural Extraction via One Auxiliary Token"

Reimagine state management with CRDTs. Make your app collaborative effortlessly.

Bridging LLM and Recommender System.

Code for our paper "Adaptive Compression For Databases" (EDBT '24)

Fast Inference of MoE Models with CPU-GPU Orchestration

Implementation of "RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation".

Code for 'LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders'

[NAACL'24] Dataset, code and models for "TableLlama: Towards Open Large Generalist Models for Tables".