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DATA_DIR="./data/data.json" |
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MODEL_INFO = ["Model Name", "Language Model"] |
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AVG_INFO = ["Avg. All"] |
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ME_INFO=["Method Name", "Language Model"] |
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KE_Data_INFO = ["FewNERD", "FewRel", "InstructIE-en", "MAVEN","WikiEvents"] |
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KE_TASK_INFO = ["Avg. All", "FewNERD", "FewRel", "InstructIE-en", "MAVEN","WikiEvents"] |
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KE_CSV_DIR = "./ke_files/result-kgc.csv" |
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DATA_COLUMN_NAMES =["locality","labels","concept","text"] |
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KE_TABLE_INTRODUCTION = """In the table below, we summarize each task performance of all the models. We use F1 score(%) as the primary evaluation metric for each tasks. |
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""" |
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RESULT_COLUMN_NAMES= ["DataSet","Metric","Metric","ICE","AdaLoRA","MEND","ROME","MEMIT","FT-L","FT"] |
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STRUCT_COLUMN_NAMES=["Datasets","ZsRE","Wikirecent","Wikicounterfact","WikiBio"] |
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DATA_STRUCT=""" |
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|Datasets |ZsRE |Wikirecent| Wikicounterfact| WikiBio| |
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|Train |10,000 |570 |1455 |592| |
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|Test |1230| 1266 |885 |1392| |
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""" |
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TITLE = """# KnowEdit: a dataset for knowledge editing""" |
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BACKGROUND=""" |
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Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising from their extensive parameterization.There is an increasing interest in efficient, lightweight methods for onthe-fly model modifications. To this end, recent years have seen a burgeoning in the techniques of knowledge editing for LLMs, which aim to efficiently modify LLMs’ behaviors within specific domains while preserving overall performance across various inputs. |
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""" |
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LEADERBORAD_INTRODUCTION = """ |
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This is the dataset for knowledge editing. It contains six tasks: ZsRE, Wiki<sub>recent</sub>, Wiki<sub>counterfact</sub>, WikiBio, ConvSent and Sanitation. This repo shows the former 4 tasks and you can get the data for ConvSent and Sanitation from their original papers. |
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""" |
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DATA_SCHEMA =""" { |
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"subject": xxx, |
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"target_new": xxx, |
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"prompt": xxx, |
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"portability":{ |
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"Logical_Generalization": [], |
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... |
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} |
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"locality":{ |
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"Relation_Specificity": [], |
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... |
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} |
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}""" |
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CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" |
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CITATION_BUTTON_TEXT = r"""@article{tan2023evaluation, |
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title={Evaluation of ChatGPT as a question answering system for answering complex questions}, |
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author={Yiming Tan and Dehai Min and Yu Li and Wenbo Li and Nan Hu and Yongrui Chen and Guilin Qi}, |
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journal={arXiv preprint arXiv:2303.07992}, |
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year={2023} |
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} |
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@article{gui2023InstructIE, |
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author = {Honghao Gui and Jintian Zhang and Hongbin Ye and Ningyu Zhang}, |
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title = {InstructIE: {A} Chinese Instruction-based Information Extraction Dataset}, |
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journal = {arXiv preprint arXiv:2303.07992}, |
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year = {2023} |
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} |
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@article{yao2023edit, |
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author = {Yunzhi Yao and Peng Wang and Bozhong Tian and Siyuan Cheng and Zhoubo Li and Shumin Deng and Huajun Chen and Ningyu Zhang}, |
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title = {Editing Large Language Models: Problems, Methods, and Opportunities}, |
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journal = {arXiv preprint arXiv:2305.13172}, |
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year = {2023} |
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} |
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""" |
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