"Query-Aware Learnable Graph Pooling Tokens as Prompt for LLMs"

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https://arxiv.org/abs/2501.17549

This paper addresses limitations in how LLMs process graph data for question answering. Existing methods either lack scalability by treating each graph node as a separate input token, or lose information by compressing the entire graph into a single token. The paper introduces a new method to improve graph question answering performance.

This paper proposes Learnable Graph Pooling Tokens (LGPT) and Early Query Fusion. LGPT uses a set of learnable tokens to represent graph information for LLMs. Early Query Fusion incorporates the question context into the graph representation process itself.

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📌 LGPT uses learnable tokens to represent graphs, moving beyond single-vector summaries. This allows LLMs to capture more nuanced graph information, improving performance by 4.13% on GraphQA.

📌 Early Query Fusion strategically incorporates query context before graph encoding. This approach focuses graph representation on task-relevant information, unlike late fusion methods.

📌 LGPT offers a practical way to handle graph complexity for LLMs. Using a fixed, small set of tokens ensures scalability, bypassing limitations of node-level graph prompting.

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Methods Explored in this Paper 🔧:

→ The paper introduces Learnable Graph Pooling Tokens (LGPT). LGPT uses learnable parameters as tokens to represent graph information for LLMs.

→ LGPT connects these learnable tokens to all nodes in the graph. Message passing through a Graph Neural Network (GNN) aggregates graph information into these tokens.

→ This method aims to balance detailed node information with global graph context, avoiding information loss seen in single-vector graph representations.

→ The paper also explores Early Query Fusion. This technique integrates the query information before encoding the graph.

→ A virtual query node is created using a text encoder and connected to all graph nodes. GNNs then process the graph incorporating this query node, creating query-aware graph embeddings.

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Key Insights 💡:

→ Node-level projection of graphs into LLMs is not scalable due to increasing graph size.

→ Graph-level projection into a single vector for LLMs leads to information loss.

→ LGPT offers a balanced approach, reducing information loss compared to single vector methods and improving scalability versus node-level projection.

→ Early Query Fusion enhances graph representation by incorporating query context earlier in the encoding process, leading to more effective graph embeddings.

→ Combining LGPT with Early Query Fusion achieves further performance improvements.

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Results 📊:

→ Achieves a 4.13% average performance improvement on the GraphQA benchmark compared to G-Retriever without training the LLM.

→ Early Query Fusion alone improves average performance by 2.88%.

→ LGPT alone improves average performance by 3.87%.