Fine-Tune, Then Rectify
Quick summary
arXiv:2511.19486v3 Announce Type: replace-cross Abstract: Driven by recent advances in artificial intelligence, a growing literature has demonstrated the potential of using large language models (LLMs) as scalable surrogates to generate human-like responses. Two common approaches to improve the performance of LLMs include: fine-tuning, which aligns the LLM more closely with human responses, and rectification, which corrects biases in LLM outputs. In this paper, we develop a two-stage framework that combines fine-tuning and rectification, and optimally allocates limited labeled samples across t
Key takeaways
- arXiv:2511.19486v3 Announce Type: replace-cross Abstract: Driven by recent advances in artificial intelligence, a growing literature has demonstrated the potential of using large language models (LLMs) as scalable surrogates to generate human-like responses.
- Two common approaches to improve the performance of LLMs include: fine-tuning, which aligns the LLM more closely with human responses, and rectification, which corrects biases in LLM outputs.
- In this paper, we develop a two-stage framework that combines fine-tuning and rectification, and optimally allocates limited labeled samples across t
Why it matters
“Fine-Tune, Then Rectify” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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