HippoRAG: Neurobiologically Inspired Long-Term Memory for LLMs (NeurIPS 2024)

Mon, 01 Jan 0001 00:00:00 +0000

Retrieval-Augmented Generation (RAG) has become the standard approach for grounding LLM outputs in external knowledge. But standard RAG has a fundamental limitation: it treats each query independently, with no memory of past retrievals or ability to connect information across documents. HippoRAG takes inspiration from the human brain’s hippocampus to overcome this, creating a long-term memory system that dramatically improves multi-hop question answering.

Published at NeurIPS 2024 and available at github.com/OSU-NLP-Group/HippoRAG, HippoRAG combines LLMs with knowledge graphs in a framework modeled on the hippocampal indexing theory of human memory. The result is a RAG system that builds a persistent knowledge structure from documents, enabling it to answer complex questions that require connecting information across multiple sources – achieving approximately 20% improvement over standard RAG on multi-hop QA benchmarks.

Rerankers: A Lightweight Python Library Unifying Ranking Methods for RAG Pipelines

Mon, 01 Jan 0001 00:00:00 +0000

Building a production-grade Retrieval-Augmented Generation (RAG) pipeline involves many decisions – which embedding model to use, which vector database, how to chunk documents, and crucially, how to rank the retrieved results. The final ranking step often makes the difference between a mediocre answer and a great one. Rerankers, an open-source Python library from AnswerDotAI (the team behind FastAI), tackles exactly this problem with an elegant, minimal interface.

Rerankers provides a unified wrapper around dozens of reranking models and methods, from classical cross-encoders to LLM-based listwise rankers and commercial API services. Its core philosophy is simple: you should be able to swap reranking strategies by changing a single line of code. This makes it invaluable for both prototyping and production RAG systems.

Information Retrieval on SoloSoft

HippoRAG: Neurobiologically Inspired Long-Term Memory for LLMs (NeurIPS 2024)

Rerankers: A Lightweight Python Library Unifying Ranking Methods for RAG Pipelines