Supplementary Material for Paper "Distilling Event Sequence Knowledge From Large Language Models"
<p>Supplementary Material for Paper:<br>Distilling Event Sequence Knowledge From Large Language Models<br>Somin Wadhwa, Oktie Hassanzadeh, Debarun Bhattacharjya, Ken Barker, and Jian Ni</p> <p>Appendix:<br>- <code>Appendix.pdf</code>: contains our prompts and a description of our human evaluation details.</p> <p>Data:<br>- <code>base_kg_v7.jsonl</code>: Our Wikidata-based Event Causal Knowledge Graph.</p> <p>Outputs:<br>- <code>sample_new_patterns_discovered.txt</code>: examples of observed new patters through application of sequential pattern mining algorithms, described in section 3.<br>- <code>precision_eval_sample.txt:</code> examples of output evaluated with a precision-evaluator model. <br>- <code>bsumm_output.txt</code>: sample outputs of identified influencing events through the application of binary summary markov model, described in section 5.2.</p> <p>Code:<br>- <code>src/generator.py</code>: ingests ICL prompts and generates requisite event sequences.<br>- <code>src/benchmarking.py</code>: ingests a _trained_ Flan-style seq2seq model to evaluate precision, and recall from the base KG.<br>- <code>src/utils.py</code>: utilities for generator and benchmarking.</p> <p>To cite:</p> <pre><code>@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}, }</code></pre>
ShareScore
28/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 12
- Reuse readiness
- 8
- Engagement
- 0