Beyond Sample-Level Feedback: Using Reference-Level Feedback to Guide Data Synthesis (2026.eacl-long)
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| Challenge: | High-quality instruction-tuning data is crucial for Large Language Models (LLMs) but it imposes a quality ceiling where models trained on the data cannot outperform the LLM generating it. |
| Approach: | They propose a paradigm that extracts desirable characteristics from carefully curated reference samples to guide the synthesis of higher-quality instruction-response pairs. |
| Outcome: | The proposed paradigm outperforms traditional sample-level feedback methods and generalizes across model architectures. |
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