Supplementary Material for Paper "Distilling Event Sequence Knowledge From Large Language Models"

Supplementary Material for Paper:Distilling Event Sequence Knowledge From Large Language ModelsSomin Wadhwa, Oktie Hassanzadeh, Debarun Bhattacharjya, Ken Barker, and Jian Ni Appendix:- Appendix.pdf: contains our prompts and a description of our human evaluation details. Data:- base_kg_v7.jsonl: Our Wikidata-based Event Causal Knowledge Graph. Outputs:- sample_new_patterns_discovered.txt: examples of observed new patters through application of sequential pattern mining algorithms, described in section 3.- precision_eval_sample.txt: examples of output evaluated with a precision-evaluator model. - bsumm_output.txt: sample outputs of identified influencing events through the application of binary summary markov model, described in section 5.2. Code:- src/generator.py: ingests ICL prompts and generates requisite event sequences.- src/benchmarking.py: ingests a _trained_ Flan-style seq2seq model to evaluate precision, and recall from the base KG.- src/utils.py: utilities for generator and benchmarking. To cite: @inproceedings{WadhwaHBBN24, author = {Somin Wadhwa and Oktie Hassanzadeh and Debarun Bhattacharjya and Ken Barker and Jian Ni}, title = {Distilling Event Sequence Knowledge From Large Language Models}, booktitle = {The Semantic Web - 23rd International Conference, {ISWC} 2024}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2024}, }