# Skill-Based Few-Shot Selection for In-Context Learning

arXiv: https://arxiv.org/abs/2305.14210 · HTML: https://mlsanity.com/paper/2305.14210/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 75 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 30 of 60)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Learning To Retrieve Prompts for In-Context Learning](https://mlsanity.com/paper/2112.08633/) (2021)
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://mlsanity.com/paper/2201.11990/) (2022)
- [Language Models are Multilingual Chain-of-Thought Reasoners](https://mlsanity.com/paper/2210.03057/) (2022)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [CodeT: Code Generation with Generated Tests](https://mlsanity.com/paper/2207.10397/) (2022)

## Built on by (most cited first, 30 of 46)

- [A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://mlsanity.com/paper/2407.04069/) (2024)
- [In-Context Learning with Iterative Demonstration Selection](https://mlsanity.com/paper/2310.09881/) (2023)
- [SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)](https://mlsanity.com/paper/2306.00739/) (2023)
- [PURPLE: Making a Large Language Model a Better SQL Writer](https://mlsanity.com/paper/2403.20014/) (2024)
- [Selective Demonstrations for Cross-domain Text-to-SQL](https://mlsanity.com/paper/2310.06302/) (2023)
- [Generative Calibration for In-context Learning](https://mlsanity.com/paper/2310.10266/) (2023)
- [Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning](https://mlsanity.com/paper/2407.03227/) (2024)
- [A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness](https://mlsanity.com/paper/2312.01082/) (2023)
- [SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models](https://mlsanity.com/paper/2311.09818/) (2023)
- [Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning](https://mlsanity.com/paper/2406.11890/) (2024)
- [Natural Language Outlines for Code: Literate Programming in the LLM Era](https://mlsanity.com/paper/2408.04820/) (2024)
- [The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think](https://mlsanity.com/paper/2505.10185/) (2025)
- [Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting](https://mlsanity.com/paper/2308.02582/) (2023)
- [LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning](https://mlsanity.com/paper/2312.04684/) (2023)
- [Bayesian Example Selection Improves In-Context Learning for Speech, Text, and Visual Modalities](https://mlsanity.com/paper/2404.14716/) (2024)
- [Demonstration Selection for In-Context Learning via Reinforcement Learning](https://mlsanity.com/paper/2412.03966/) (2024)
- [Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction](https://mlsanity.com/paper/2402.13741/) (2024)
- [PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL](https://mlsanity.com/paper/2409.14082/) (2024)
- [PII-Scope: A Comprehensive Study on Training Data PII Extraction Attacks in LLMs](https://mlsanity.com/paper/2410.06704/) (2024)
- [Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification](https://mlsanity.com/paper/2410.10756/) (2024)
- [The Role of Diversity in In-Context Learning for Large Language Models](https://mlsanity.com/paper/2505.19426/) (2025)
- [Unveiling Chain of Step Reasoning for Vision-Language Models with Fine-grained Rewards](https://mlsanity.com/paper/2509.19003/) (2025)
- [You only need 4 extra tokens: Synergistic Test-time Adaptation for LLMs](https://mlsanity.com/paper/2510.10223/) (2025)
- [Automatic Combination of Sample Selection Strategies for Few-Shot Learning](https://mlsanity.com/paper/2402.03038/) (2024)
- [AdaptMI: Adaptive Skill-based In-context Math Instruction for Small Language Models](https://mlsanity.com/paper/2505.00147/) (2025)
- [Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection](https://mlsanity.com/paper/2410.14049/) (2024)
- [The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare](https://mlsanity.com/paper/2411.03287/) (2024)
- [PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation](https://mlsanity.com/paper/2412.12447/) (2024)
- [Learning to Search Effective Example Sequences for In-Context Learning](https://mlsanity.com/paper/2503.08030/) (2025)
- [PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data](https://mlsanity.com/paper/2512.11013/) (2025)

## Abstract (arXiv)

In-context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each test instance separately -- is important for in-context learning. In this paper, we propose Skill-KNN, a skill-based few-shot selection method for in-context learning. The key advantages of Skill-KNN include: (1) it addresses the problem that existing methods based on pre-trained embeddings can be easily biased by surface natural language features that are not important for the target task; (2) it does not require training or fine-tuning of any models, making it suitable for frequently expanding or changing example banks. The key insight is to optimize the inputs fed into the embedding model, rather than tuning the model itself. Technically, Skill-KNN generates the skill-based descriptions for each test case and candidate example by utilizing a pre-processing few-shot prompting, thus eliminating unimportant surface features. Experimental results across five cross-domain semantic parsing datasets and six backbone models show that Skill-KNN significantly outperforms existing methods.