Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, Xipeng Qiu
| Challenge: | In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction. |
| Approach: | They propose a single model to retrieve demonstrations for a wide range of tasks by combining training signals from various tasks into a unified list-wise ranking formulation by language model’s feedback. |
| Outcome: | The proposed model outperforms baselines on 30+ tasks across 13 task families and multiple data domains. |
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Enhancing In-Context Learning via Implicit Demonstration Augmentation (2024.acl-long)
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| 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. |
MDR: Model-Specific Demonstration Retrieval at Inference Time for In-Context Learning (2024.naacl-long)
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| Challenge: | Existing methods for retrieval-based in-context learning ignore model biases and fail to retrieve the most appropriate demonstrations for different LLMs. |
| Approach: | They propose a model-specific demonstration retrieval method that considers the biases of different LLMs at inference time. |
| Outcome: | The proposed method improves performance on seen and unseen tasks with multi-scale inference LLMs by up to 41.2%. |
Learning to Retrieve In-Context Examples for Large Language Models (2024.eacl-long)
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| Challenge: | Existing approaches to improve in-context learning performance are highly sensitive to the quality of the incontext examples provided. |
| Approach: | They propose a framework to iteratively train dense retrievers that can identify high-quality in-context examples for large language models. |
| Outcome: | The proposed model improves performance by retrieving examples with similar patterns, and the gains are consistent across LLMs of varying sizes. |
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER (2022.acl-long)
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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. |
| Approach: | They propose a demonstration-based learning method which lets the input be prefaced by task demonstrations for in-context learning. |
| Outcome: | The proposed method improves on in-domain learning and domain adaptation in low-resource settings. |
The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis (2024.findings-acl)
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Miaoran Zhang, Vagrant Gautam, Mingyang Wang, Jesujoba Alabi, Xiaoyu Shen, Dietrich Klakow, Marius Mosbach
| Challenge: | In-context learning is a popular inference strategy where large language models solve a task using only a few labeled demonstrations without updating the model parameters. |
| Approach: | They conduct multidimensional analysis of multilingual in-context learning using 5 models from different model families and 9 datasets covering classification and generation tasks. |
| Outcome: | The results show that demonstrations vary significantly across models, tasks, and languages. |
DRUM: Learning Demonstration Retriever for Large MUlti-modal Models (2025.acl-srw)
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| Challenge: | Recent studies have shown that large language models (LLMs) have impressive capabilities in dealing with new tasks with the help of in-context learning (ICL). |
| Approach: | They propose to concate the image and text embeddings to enhance the retrieval performance of a visual-language task and to calculate a list-wise ranking loss for training the embeddable model. |
| Outcome: | The proposed framework fine-tunes the CLIP embedding model to better meet the needs of the large vision-language models. |
Learning to Retrieve Iteratively for In-Context Learning (2024.emnlp-main)
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Yunmo Chen, Tongfei Chen, Harsh Jhamtani, Patrick Xia, Richard Shin, Jason Eisner, Benjamin Van Durme
| Challenge: | In-context learning is a powerful tool for learning large language models. |
| Approach: | They propose an iterative retrieval framework that empowers retrievers to make iterable decisions through policy optimization. |
| Outcome: | The proposed framework outperforms existing methods on semantic parsing datasets with 4M additional parameters for state encoding. |
The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation (2024.findings-naacl)
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| Challenge: | Large language models (LLMs) have shown excellent performance on various NLP tasks. |
| Approach: | They propose a method that integrates multiple demonstration users into one aggregated demonstration to improve sequential recommendation. |
| Outcome: | The proposed method outperforms state-of-the-art LLM-based sequential recommendation methods on three recommendation datasets. |
XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples (2025.findings-naacl)
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| Challenge: | XAMPLER: Cross-Lingual Example Retrieval is a cross-lingual example retrieval method . large language models (LLMs) have emerged as effective in-context learning methods . |
| Approach: | They propose a method to train a multilingual model with annotated English examples . they use annotized English data to train the model and use it to train other languages . |
| Outcome: | XAMPLER: Cross-Lingual Example Retrieval improves in-context learning in English . it trains a retriever based on a multilingual small language model using annotated English examples . |
Topic Coverage-based Demonstration Retrieval for In-Context Learning (2025.emnlp-main)
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| 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. |