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examples/llm/README.md

@@ -5,8 +5,8 @@
 | [`g_retriever.py`](./g_retriever.py)         | Example for Retrieval-Augmented Generation (RAG) w/ GNN+LLM by co-training `LLAMA2` with `GAT` for answering questions based on knowledge graph information |
 | [`g_retriever_utils/`](./g_retriever_utils/) | Contains multiple scripts for benchmarking GRetriever's architecture and evaluating different retrieval methods.                                            |
 
-| [`nvtx_examples/`](./nvtx_examples/)         | Contains examples of how to wrap functions using the NVTX profiler for CUDA runtime analysis.                                                                                                  |
-| [`molecule_gpt.py`](./molecule_gpt.py)       | Example for MoleculeGPT: Instruction Following Large Language Models for Molecular Property Prediction                                                                                         |
-| [`glem.py`](./glem.py)                       | Example for [GLEM](https://arxiv.org/abs/2210.14709), a GNN+LLM co-training model via variational Expectation-Maximization (EM) framework on node classification tasks to achieve SOTA results |
-| [`git_mol.py`](./git_mol.py)                 | Example for GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text                                                                                      |
-| [`hotpot_qa.py`](./hotpot_qa.py)             | Example for converting adapting the retrieval step of conventional Retrieval-Augmented Generation (RAG) for use with G-retriever, and how to approximate the precision/recall of a subgraph retrieval method. Uses the HotPotQA dataset as an example since it is multihop in nature. |
+| [`nvtx_examples/`](./nvtx_examples/) | Contains examples of how to wrap functions using the NVTX profiler for CUDA runtime analysis. |
+| [`molecule_gpt.py`](./molecule_gpt.py) | Example for MoleculeGPT: Instruction Following Large Language Models for Molecular Property Prediction |
+| [`glem.py`](./glem.py) | Example for [GLEM](https://arxiv.org/abs/2210.14709), a GNN+LLM co-training model via variational Expectation-Maximization (EM) framework on node classification tasks to achieve SOTA results |
+| [`git_mol.py`](./git_mol.py) | Example for GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text |
+| [`hotpot_qa.py`](./hotpot_qa.py) | Example for converting adapting the retrieval step of conventional Retrieval-Augmented Generation (RAG) for use with G-retriever, and how to approximate the precision/recall of a subgraph retrieval method. Uses the HotPotQA dataset as an example since it is multihop in nature. |

examples/llm/hotpot_qa.py

@@ -124,7 +124,7 @@
     We will use precision as a proxy for recall. This is because for recall,
     we must know how many relevant triples exist for each question,
     but this is not known.
-    
+
     Note that the retrieval precision may be much lower than expected.
     likely due to a bug in retriever causing similar issue here:
     https://github.com/pyg-team/pytorch_geometric/pull/9806