SRCH:C74A5A56
CodeT5 Pretraining Data Ratios and Few-Shot Learning on HumanEval and MBPP
Abstract
Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the ratio of code-to-natural-language pretraining data in CodeT5 affect its few-shot learning performance on the HumanEval dataset compared to MBPP for Python and JavaScript. 10 claims were extracted from source literature; 2 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.3/10. This report is a machine-generated literature synthesis and does not constitute original research.
Research Question
How does the ratio of code-to-natural-language pretraining data in CodeT5 affect its few-shot learning performance on the HumanEval dataset compared to MBPP for Python and JavaScript?
Verification Level
| Paper level | L2, Source-grounded claims | |
| Source-grounded claims | 10 | |
| Claim record source | parsed source sections |
Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.
Quality Tier
| Tier | Watchlist | |
| Basis | Review score or public verified-claim signal is below DOI-grade threshold. |
Descriptive public triage only; this tier does not alter current publication or DOI behavior.
Quality Dimensions
| Evidence strength | LOW | |
| Citation grounding | MEDIUM | |
| Uncertainty disclosure | MEDIUM | |
| Reproducibility status | MEDIUM |
Automated triage signals derived from public fields; not human peer review or independent validation.
Correction Record
| Status | CURRENT |
| Correction count | 0 |
| Manifest contract | paper-manifest-v1.1 |
| Correction contract | correction-record-v1 |
Public corrections are additive records. Current status does not claim the synthesis is error-free.
Provenance
| Publisher | Assignee Research |
| Public provenance | L3, Claim aggregate record |
| Report artifact | Available |
| External record | Not registered |
| Claim lineage | 10 aggregate source-grounded claims |
| Review method | Automated multi-reviewer assessment |
| Quality guide | How to read scores, claims, manifests, and evidence links |
| Provenance contract | source-provenance-v1 |
| Note | Machine-generated synthesis of existing literature. Not primary research. |