@inproceedings{maosongcao-etal-2025-condor,
title = "Condor: Enhance {LLM} Alignment with Knowledge-Driven Data Synthesis and Refinement",
author = "Cao, Maosong and
Zhang, Taolin and
Li, Mo and
Zhang, Chuyu and
Liu, Yunxin and
Duan, Haodong and
Zhang, Songyang and
Chen, Kai",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1091/",
doi = "10.18653/v1/2025.acl-long.1091",
pages = "22392--22412",
ISBN = "979-8-89176-251-0",
abstract = "The quality of Supervised Fine-Tuning (SFT) data plays a critical role in enhancing the conversational capabilities of Large Language Models (LLMs). However, the availability of high-quality human-annotated SFT data has become a significant bottleneck for LLMs, necessitating a greater reliance on synthetic training data. In this work, we introduce Condor, a two-stage synthetic data generation framework that incorporates World Knowledge Trees and Self-Reflection Refinement to produce high-quality SFT data at scale. Our experimental results demonstrate that a base model fine-tuned on only 20K Condor-generated samples achieves superior performance compared to instruct model trained with RLHF. The additional refinement stage in Condor further enables iterative self-improvement for LLMs at various scales (up to 72B), validating the effectiveness of our approach. Furthermore, our investigation into the scaling of synthetic data in post-training reveals substantial unexplored potential for performance improvements, opening promising avenues for future research."
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<abstract>The quality of Supervised Fine-Tuning (SFT) data plays a critical role in enhancing the conversational capabilities of Large Language Models (LLMs). However, the availability of high-quality human-annotated SFT data has become a significant bottleneck for LLMs, necessitating a greater reliance on synthetic training data. In this work, we introduce Condor, a two-stage synthetic data generation framework that incorporates World Knowledge Trees and Self-Reflection Refinement to produce high-quality SFT data at scale. Our experimental results demonstrate that a base model fine-tuned on only 20K Condor-generated samples achieves superior performance compared to instruct model trained with RLHF. The additional refinement stage in Condor further enables iterative self-improvement for LLMs at various scales (up to 72B), validating the effectiveness of our approach. Furthermore, our investigation into the scaling of synthetic data in post-training reveals substantial unexplored potential for performance improvements, opening promising avenues for future research.</abstract>
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%0 Conference Proceedings
%T Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement
%A Cao, Maosong
%A Zhang, Taolin
%A Li, Mo
%A Zhang, Chuyu
%A Liu, Yunxin
%A Duan, Haodong
%A Zhang, Songyang
%A Chen, Kai
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F maosongcao-etal-2025-condor
%X The quality of Supervised Fine-Tuning (SFT) data plays a critical role in enhancing the conversational capabilities of Large Language Models (LLMs). However, the availability of high-quality human-annotated SFT data has become a significant bottleneck for LLMs, necessitating a greater reliance on synthetic training data. In this work, we introduce Condor, a two-stage synthetic data generation framework that incorporates World Knowledge Trees and Self-Reflection Refinement to produce high-quality SFT data at scale. Our experimental results demonstrate that a base model fine-tuned on only 20K Condor-generated samples achieves superior performance compared to instruct model trained with RLHF. The additional refinement stage in Condor further enables iterative self-improvement for LLMs at various scales (up to 72B), validating the effectiveness of our approach. Furthermore, our investigation into the scaling of synthetic data in post-training reveals substantial unexplored potential for performance improvements, opening promising avenues for future research.
%R 10.18653/v1/2025.acl-long.1091
%U https://aclanthology.org/2025.acl-long.1091/
%U https://doi.org/10.18653/v1/2025.acl-long.1091
%P 22392-22412
Markdown (Informal)
[Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement](https://aclanthology.org/2025.acl-long.1091/) (Cao et al., ACL 2025)
ACL
- Maosong Cao, Taolin Zhang, Mo Li, Chuyu Zhang, Yunxin Liu, Haodong Duan, Songyang Zhang, and Kai Chen. 2025. Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22392–22412, Vienna, Austria. Association for Computational Linguistics.