Abstract
In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour. Despite the importance, there is no public tool that supports such analysis of pre-training corpora at large scale. To help research in this space, we launch Koala, a searchable index over large pre-training corpora using lossless compressed suffix arrays with highly efficient compression rate and search support. In its first release we index the public proportion of OPT 175B, GPT-3, GPT-Neo, GPT-Neo, LLaMA, BERT, ELECTRA, RoBERTA, XLNet pre-training corpora. Koala provides a framework to do forensic analysis on the current and future benchmarks as well as to assess the degree of memorization in the output from the LLMs. Koala is available for public use at https://koala-index.erc.monash.edu/.
| Original language | English |
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| Title of host publication | Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations |
| Editors | Yansong Feng, Els Lefever |
| Place of Publication | Stroudsburg PA USA |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 90-98 |
| Number of pages | 9 |
| ISBN (Electronic) | 9788891760677 |
| Publication status | Published - 2023 |
| Event | Empirical Methods in Natural Language Processing 2023 - , Singapore Duration: 6 Dec 2023 → 10 Dec 2023 https://2023.emnlp.org/ https://aclanthology.org/volumes/2023.findings-emnlp/ (Proceedings) https://aclanthology.org/volumes/2023.emnlp-demo/ (Proceedings) |
Conference
| Conference | Empirical Methods in Natural Language Processing 2023 |
|---|---|
| Abbreviated title | EMNLP 2023 |
| Country/Territory | Singapore |
| Period | 6/12/23 → 10/12/23 |
| Internet address |