We lifted 8 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| wyl-willing/MindMap | canonical | 6 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| chat | Ran | wyl-willing/MindMap/pre-training/explainpe/keyword.py pointer only (licence: NONE) · get_code("b759881245a90f3e") |
| chat_35 | Ran | wyl-willing/MindMap/MindMap.py pointer only (licence: NONE) · get_code("5e07cc24a99e649e") |
| chat_4 | Ran | wyl-willing/MindMap/MindMap.py pointer only (licence: NONE) · get_code("c6c612f4e7f41ed4") |
| prompt_comparation | Ran | wyl-willing/MindMap/evaluation/gpt4_preference_drug.py pointer only (licence: NONE) · get_code("7be86cacefea6f89") |
| prompt_comparation | Ran | wyl-willing/MindMap/evaluation/gpt4_preference_total.py pointer only (licence: NONE) · get_code("f826b5a33b16da28") |
| row_to_string | Ran | wyl-willing/MindMap/pre-training/chatdoctor5k/csvTodocument.py pointer only (licence: NONE) · get_code("0276dad790bbbcfc") |
| is_unable_to_answer | Not yet run | wyl-willing/MindMap/pre-training/chatdoctor5k/csvTodocument.py pointer only (licence: NONE) · get_code("ba946e72117a1df4") |
| prompt_comparation | Not yet run | wyl-willing/MindMap/evaluation/gpt4_preference_disease.py pointer only (licence: NONE) · get_code("eb72fd9471b417f2") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks. However, they often suffer from limitations such as difficulty in incorporating new knowledge, generating hallucinations, and explaining their reasoning process. To address these challenges, we propose a novel prompting pipeline, named \method, that leverages knowledge graphs (KGs) to enhance LLMs' inference and transparency. Our method enables LLMs to comprehend KG inputs and infer with a combination of implicit and external knowledge. Moreover, our method elicits the mind map of LLMs, which reveals their reasoning pathways based on the ontology of knowledge. We evaluate our method on diverse question \& answering tasks, especially in medical domains, and show significant improvements over baselines. We also introduce a new hallucination evaluation benchmark and analyze the effects of different components of our method. Our results demonstrate the effectiveness and robustness of our method in merging knowledge from LLMs and KGs for combined inference. To reproduce our results and extend the framework further, we make our codebase available at https://github.com/wyl-willing/MindMap.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2308.09729")
get_code_for_paper("2308.09729")
have("2308.09729")
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