UCS: Estimating Unseen Coverage for Improved In-Context Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing selection methods prioritize heuristic notions of relevance or diversity and provide limited insight into the coverage of a demonstration set. |
| Approach: | They propose a training-free, subset-level coverage prior that is unrevealed by a model-consistent embedding and a Smoothed Good-Turing estimator to estimate the number of unrevelled clusters within a candidate subset. |
| Outcome: | Experiments on multiple intent-classification and reasoning benchmarks show that augmenting strong baselines with UCS improves ICL accuracy by 2-6% under the same selection budget. |
Similar Papers
Coverage-based Example Selection for In-Context Learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | In-context learning (ICL) is a training-free paradigm of fewshot inference that can generalize to novel tasks by conditioning on a few task examples. |
| Approach: | They show that BERTScore-Recall (BSR) selects better examples that demonstrate more of the salient aspects of the test input. |
| Outcome: | The proposed model outperforms methods that leverage task or LLM-specific training on compositional tasks. |
In-Context Learning with Iterative Demonstration Selection (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing literature has highlighted the importance of selecting examples that are diverse or semantically similar to the test sample . Existing studies have shown that the optimal selection dimension, i.e., diversity or similarity, is task-specific. |
| Approach: | They propose to use zero-shot chain-of-thought reasoning to iteratively select examples that are diverse but still strongly correlated with the test sample as ICL demonstrations. |
| Outcome: | The proposed method outperforms existing demonstration selection methods on reasoning, question answering, and topic classification tasks. |
CoverICL: Selective Annotation for In-Context Learning via Active Graph Coverage (2024.emnlp-main)
Copied to clipboard
Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, George Karypis
| Challenge: | In-context learning (ICL) uses few-shot labeled examples to perform selective annotation. |
| Approach: | They propose an algorithm that incorporates uncertainty sampling into selective annotation for ICL . CoverICL builds a nearest-neighbor graph based on the semantic similarity between candidate ICL examples . |
| Outcome: | The proposed algorithm outperforms existing methods for low-budget active learning (AL) it is up to 2x more budget-efficient than SOTA methods for high-budge AL. |
Enhancing In-Context Learning via Implicit Demonstration Augmentation (2024.acl-long)
Copied to clipboard
| Challenge: | In-context learning (ICL) is a new paradigm for pre-trained language models that can make predictions for unseen inputs without updating parameters. |
| Approach: | They propose a method that enables a model to augmented copies of a demonstration by leveraging their deep feature distribution and a logit calibration mechanism. |
| Outcome: | The proposed method significantly improves the average and worst-case accuracy across diverse PLMs and tasks. |
SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation (2024.emnlp-main)
Copied to clipboard
| Challenge: | In-context learning improves performance of large language models (LLMs) performance of ICL highly depends on quality of demonstrations . |
| Approach: | They propose a syntactic-augmented COverage-based In-context example selection strategy that leverages syntastic knowledge beyond word matching to select better examples for machine translation. |
| Outcome: | The proposed strategy obtains the highest average COMET score among learning-free methods. |
Topic Coverage-based Demonstration Retrieval for In-Context Learning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Prior methods to retrieve demonstrations based on embedding similarity or generation probability, resulting in irrelevant or redundant examples. |
| Approach: | They propose a topic coverage-based retrieval framework that selects demonstrations to comprehensively cover topic-level knowledge relevant to both the test input and the model. |
| Outcome: | The proposed framework covers all the necessary knowledge for the test input and the model. |
Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding (2025.acl-short)
Copied to clipboard
| Challenge: | Prior research has found that large language models overlook input-label mapping information in ICL, relying more on their pre-trained knowledge. |
| Approach: | They propose a novel method that contrasts input-label mappings between positive and negative in-context examples to improve model performance. |
| Outcome: | The proposed method improves performance on 7 natural language understanding tasks without additional training. |
Demonstration Augmentation for Zero-shot In-context Learning (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations without the need for parameter updates. |
| Approach: | They propose to use model’s previously predicted historical samples as demonstrations for subsequent ones to improve model’ s performance. |
| Outcome: | The proposed method significantly outperforms the previous method and its predecessors in terms of inference cost and time. |
UniICL: An Efficient ICL Framework Unifying Compression, Selection, and Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to improve reasoning abilities of Large Language Models (LLMs) have limitations due to excessive growth in context length, causing large hardware burden. |
| Approach: | They propose a novel Unified ICL framework that unifies demonstration compression, demonstration selection, and final response generation. |
| Outcome: | The proposed framework unifies demonstration compression, demonstration selection, and final response generation. |
Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown striking ability to adapt to target tasks with a few input-output demonstrations. |
| Approach: | They propose a framework which bootstraps LMs’ intrinsic capabilities to perform zero-shot ICL. |
| Outcome: | The proposed framework outperforms baselines on 23 BIG-Bench Hard tasks on average accuracy and head-to-head comparison. |