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Xianzhen Luo

ACL 2026×2ICLR 2026评测与基准代码效率与系统数据与预训练推理
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一作贡献70
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合作网络6
2 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

3
一作ACL 2026Poster· Natural Language Processing

Scaling Laws for Code: A More Data-Hungry Regime

Xianzhen Luo · Wenzhen Zheng · Qingfu Zhu · Rongyi Zhang · Houyi Li · Siming Huang · YuanTao Fan · Wanxiang Che

摘要

Code Large Language Models (LLMs) are revolutionizing software engineering. However, scaling laws that guide the efficient training are predominantly analyzed on Natural Language (NL). Given the fundamental differences like strict syntax between code and NL, it is unclear whether these laws are d...

代码效率与系统数据与预训练OpenReview 项目页
作者位次 #5/10ACL 2026Findings· Findings of ACL

Format-Adapter: Improving Reasoning Capability of LLM s by Adapting Suitable Format

Dingzirui Wang · Xuanliang Zhang · Rongyu Cao · Longxu Dou · Xianzhen Luo · YINGWEI MA · Qingfu Zhu · Binhua Li · Fei Huang · Yongbin Li

摘要

Generating and voting multiple answers is an effective method to mitigate reasoning inconsistencies of large language models (LLMs). Prior works have shown that multiple reasoning formats outperform a single format when generating multiple answers. However, previous works using multiple formats r...

推理评测与基准社会与人本OpenReview 项目页

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