pith:NWZ2LUZQ
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
The same problem intent given to CodeGen in multiple turns produces substantially better programs than a single turn on the MTPB benchmark.
arxiv:2203.13474 v5 · 2022-03-25 · cs.LG · cs.CL · cs.PL
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Claims
Our analysis on MTPB shows that the same intent provided to CODEGEN in multi-turn fashion significantly improves program synthesis over that provided as a single turn.
The multi-turn prompts in MTPB accurately capture natural human problem decomposition without introducing selection bias or making the benchmark easier than real-world use cases.
CodeGen models match prior SOTA on HumanEval zero-shot code generation, and multi-turn prompting on the new MTPB benchmark significantly improves synthesis over single-turn inputs.
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| First computed | 2026-07-05T05:46:14.025352Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/NWZ2LUZQPZPVMV62NUPT4I7GKI \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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