Xuanli He, Gholamreza Haffari, Thuy-Trang Vu, Ehsan Shareghi
We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| ekzhu/datasketch | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| optimal_partitions | Ran | ekzhu/datasketch/datasketch/lshensemble_partition.py code served (permissive licence) · get_code("bd7d7a9c3a6709ee") |
| sha1_hash32 | Ran | ekzhu/datasketch/datasketch/hashfunc.py code served (permissive licence) · get_code("6dc9da353c1b85b0") |
| sha1_hash64 | Ran | ekzhu/datasketch/datasketch/hashfunc.py code served (permissive licence) · get_code("9c25f231de344715") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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 pretraining 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/.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2303.14770")
get_code_for_paper("2303.14770")
have("2303.14770")
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