These are some LLM-assisted exploration notes from the paper HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models

The Multi-Hop Problem in RAGs

The idea of “hops” are important in RAG.

Consider this example.

Question 1:

“Who wrote Hamlet?”

→ The answer (Shakespeare) is in one document.*

Question 2:

“Which university did the president of OpenAI attend?”

• Step 1: Retrieve information on who the president of OpenAI is (e.g., Greg Brockman).
• Step 2: Retrieve information on which university Greg Brockman attended (e.g., MIT).

How HippoRAG Achieves a Single-Step Multi-Hop Retrieval

Traditional RAG solutions, such as IRCoT (Iterative Retrieval Chain-of-Thought) depend on iterative retrieval - kind of like looking up docs in a loop.

With HippoRAG, two mechanisms are combined to compress these multiple hops into one:

• Building a knowledge graph (KG) where concepts and relationships are indexed.
• Using Personalized PageRank (PPR) to retrieve relevant paths across multiple documents in one query.

The benefits of the above combination makes HippoRAG:

• Faster (avoids iterative retrieval)
• More accurate (find connections that isolated retrieval steps miss)
• Cheaper (reduce API calls and computation)

How HippoRAG Builds Its Knowledge Graph (KG)

HippoRAG constructs a schemaless knowledge graph from a text corpus by leveraging large language models (LLMs) for Open Information Extraction (OpenIE) and retrieval encoders for linking entities. This process enables multi-hop reasoning in a single retrieval step.

1. Offline Indexing (Building the Graph)

This step is analogous to how the human neocortex encodes memory.

✅ Extract Knowledge Graph Triples

• Uses an instruction-tuned LLM (e.g., GPT-3.5) to extract subject-predicate-object triples from text.
  • Example:
    Input Passage: “Steve Jobs co-founded Apple in 1976.”
    Extracted Triples:
    • (Steve Jobs, co-founded, Apple)
    • (Apple, founded_in, 1976)

✅ Create Graph Nodes & Edges

• Nodes = extracted entities (noun phrases) (e.g., Steve Jobs, Apple).
• Edges = relationships between entities (e.g., co-founded).

✅ Synonymy Linking (Parahippocampal Processing)

• Uses retrieval encoders (e.g., Contriever, ColBERTv2) to identify similar entities (e.g., “USA” = “United States”).
• Creates extra edges to connect synonyms, improving retrieval robustness.

✅ Store the Graph

• The final knowledge graph consists of:
  • Nodes (Entities)
  • Edges (Relations & Synonyms)
  • Passage Mapping (Each node is linked to the original text passage for retrieval)

2. Online Retrieval (Querying the Graph)

This step mimics the hippocampus retrieving memories.

✅ Extract Query Named Entities

• The LLM identifies key entities in the query.
• Example: “Which Stanford professor works on Alzheimer’s?”
  • Query Entities: {Stanford, Alzheimer’s}

✅ Find Related Nodes in the Knowledge Graph

• Uses retrieval encoders to find graph nodes most similar to the query entities.
• Example: The query {Stanford, Alzheimer’s} matches the node {Thomas Südhof} in the KG.

✅ Personalized PageRank (PPR) for Multi-Hop Retrieval

• Runs Personalized PageRank (PPR) on the graph using query nodes as starting points.
• Spreads probability over connected nodes, enabling multi-hop reasoning.
• Example:
  • {Stanford} → {Thomas Südhof}
  • {Alzheimer’s} → {Thomas Südhof}
  • Final Retrieval: Thomas Südhof is a Stanford professor working on Alzheimer’s.

✅ Retrieve & Rank Passages

• The most relevant passages are selected based on PPR scores.

How HippoRAG Uses PageRank to Order Results

1. Convert Text to a Graph

   • Extract entities (nodes) and relationships (edges).
   • Example:

   1 2	(Stanford, employs, Thomas Südhof) (Thomas Südhof, researches, Alzheimer’s)

2. Find Relevant Nodes

   • If the query is: “Which Stanford professor studies Alzheimer’s?”
   • The query matches {Stanford, Alzheimer’s} in the graph.

3. Run Personalized PageRank (PPR)

   • Give high starting scores to query nodes (Stanford and Alzheimer’s).
   • Spread scores to connected nodes (e.g., Thomas Südhof gets a high score).

4. Rank Passages by PageRank Score

   • Passages mentioning Thomas Südhof get top rank.
   • Less relevant passages rank lower.

Why This Works

• Finds indirect connections (multi-hop retrieval).
• Ranks based on real-world relevance rather than keyword matching.
• Fast, since it’s done in one step.