Challenge: Domain- and customer-specific requirements complicate the problem of NL2SQL customization.
Approach: They propose a distilled customization framework tailored for NL2SQL tasks.
Outcome: The proposed framework outperforms teacher models on three benchmarks and achieves an average improvement of 36% in execution accuracy.

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Challenge: Existing studies on distilled lightweight LLMs have focused on transferring knowledge from a larger model (the teacher) to a smaller model (sector).
Approach: They propose a family of distilled, lightweight LLMs derived from Qwen2.5 models.
Outcome: Experimental results show that the distilled models have significantly stronger instruction-following capabilities than the original models.
Effective Distillation of Table-based Reasoning Ability from LLMs (2024.lrec-main)

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Challenge: Existing work on table-based reasoning distillation has focused on smaller models with limited performance.
Approach: They propose a table-based reasoning distillation approach to distill LLMs into smaller models . their results show that a 220 million parameter model fine-tuned using distilled data improves performance .
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Text2Sql: Pure Fine-Tuning and Pure Knowledge Distillation (2025.naacl-industry)

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Challenge: Text2Sql is a task that translates natural language questions and database schemas into SQL queries.
Approach: They employ pure fine-tuning strategy to reduce redundancy by using only 53% of the baseline prompt length to fine- tune the model.
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Synthesizing Text-to-SQL Data from Weak and Strong LLMs (2024.acl-long)

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Challenge: a capability gap exists between open-source and closed-source large language models (LLMs) . the adoption of closed-sourced LLMs introduces concerns pertaining to openness, privacy, and substantial costs.
Approach: They propose a synthetic data approach that combines strong and weak models for error information . they demonstrate the effectiveness of SENSE, a specialized text-to-SQL model .
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Personalized Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation (2023.emnlp-main)

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Challenge: Recent studies have shown that close-sourced LLMs lack the ability to integrate into real-world applications due to their high associated costs and ethical concerns.
Approach: They propose to use student model to refine its own solution by querying ChatGPT to generate task instruction and solution pairs and querying data to refine model.
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Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation (2022.coling-1)

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Challenge: Large-scale pretrained language models have led to significant improvements in Natural Language Processing, but they come at the cost of high computational and storage requirements.
Approach: They propose to distill knowledge from larger models to smaller ones through pseudo-labels on task-specific datasets.
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Streamlining LLMs: Adaptive Knowledge Distillation for Tailored Language Models (2025.naacl-srw)

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Challenge: Large language models (LLMs) have transformative potential across industries, e.g., enhancing customer service, revolutionizing medical diagnostics, or identifying crises in news articles.
Approach: They propose to distill compact, parameter-efficient tailored language models from LLMs for domain-specific tasks with comparable performance.
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Mind’s Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models (2024.naacl-long)

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Challenge: Large language models (LLMs) have achieved significant advances in natural language processing, but their scale and computational demands pose challenges to their practical application.
Approach: They propose a method for distilling the self-evaluation capability from LLMs into SLMs and advocate for more comprehensive thinking by incorporating multiple distinct CoTs and self-estimation outputs.
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DistillCSE: Distilled Contrastive Learning for Sentence Embeddings (2023.findings-emnlp)

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Challenge: Existing approaches to sentence embeddings are based on contrastive learning (CL) .
Approach: They propose a framework which performs contrastive learning under the self-training paradigm with knowledge distillation and propose 'Group-P shuffling strategy' and averaging logits from multiple teacher components.
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SCoder: Progressive Self-Distillation for Bootstrapping Small-Scale Data Synthesizers to Empower Code LLMs (2025.findings-emnlp)

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Challenge: Existing code large language models rely on large-scale instruction data distilled from proprietary LLMs for fine-tuning, which typically incurs high costs.
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