Papers with -shot regime
Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning (2022.naacl-industry)
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| Challenge: | Medical coding (MC) is an essential pre-requisite for reliable data retrieval and reporting. |
| Approach: | They propose a method to classify medical terms into standardized alphanumerical terms and codes . they use a combination of traditional BERT-based classification and a zero/few-shot learning approach . |
| Outcome: | The proposed approach outperforms baselines in the few-shot regime. |
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data (2024.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a core component of natural language processing, present in a variety of applications such as medical coding, financial news analysis, or legal documents parsing. |
| Approach: | They propose to use Large Language Models (LLMs) to create NuNER, a compact language representation model specialized in the Named Entity Recognition task. |
| Outcome: | The proposed model outperforms similar-sized foundation models in the few-shot regime and is based on a human-annotated dataset. |
AMAL: Meta Knowledge-Driven Few-Shot Adapter Learning (2022.emnlp-main)
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| Challenge: | Existing methods for fine-tuning pre-trained language models fail to yield meaningful results in the few-shot regime. |
| Approach: | They propose a meta-learning-driven low-rank adapter pooling method for leveraging pre-trained language models even with just a few data points. |
| Outcome: | The proposed method outperforms previous few-shot learning methods on five text classification benchmark datasets. |
PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection (2026.findings-acl)
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| Challenge: | Existing reasoning models are limited by inefficiency and computational redundancy . PRISM-MCTS integrates a process reward model with a dynamic shared memory . |
| Approach: | They propose a reasoning framework that integrates a process reward model with a dynamic shared memory. |
| Outcome: | PRISM-MCTS integrates a process reward model with a dynamic shared memory . it halves trajectory requirements on GPQA while surpassing MCTS-RAG and Search-o1 . |