SIEVE: Sample-Efficient Parametric Learning from Natural Language

• URL: http://arxiv.org/abs/2604.02339v1
• Date: Mon, 02 Feb 2026 18:52:52 GMT
• Title: SIEVE: Sample-Efficient Parametric Learning from Natural Language
• Abstract summary: We propose SIEVE, a method for sample-efficient parametric learning from natural language context.<n>We use a novel synthetic data generation pipeline, SIEVE-GEN, that leverages the insight that context is decomposable.<n>Our results show that SIEVE outperforms prior context distillation methods using just three query examples.
• Score: 66.46967795931559
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural language context-such as instructions, knowledge, or feedback-contains rich signal for adapting language models. While in-context learning provides adaptation via the prompt, parametric learning persists into model weights and can improve performance further, though is data hungry and heavily relies on either high-quality traces or automated verifiers. We propose SIEVE, a method for sample-efficient parametric learning from natural language context that requires as few as three query examples. SIEVE uses a novel synthetic data generation pipeline, SIEVE-GEN, that leverages the insight that context is decomposable. Decomposing context allows us to generate higher quality rollouts by pairing synthetic queries with only the applicable context rather than the entirety, then using context distillation to internalize context into the model. We evaluate in reasoning settings where context is necessary, including custom domains and the RuleArena and Machine Translation from One Book tasks. Our results show that SIEVE outperforms prior context distillation methods using just three query examples, demonstrating how to achieve sample-efficient parametric learning from natural language.

Related papers

• Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding [53.63482987410292]
  We present a self-adaptive curriculum learning paradigm that prioritizes fine-tuning examples based on difficulty scores predicted by pre-trained language models.<n>We evaluate our method on four natural language understanding (NLU) datasets covering both binary and multi-class classification tasks.
  arXiv  Detail & Related papers  (2025-07-13T19:36:17Z)
• Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu [53.437954702561065]
  In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT.<n>This study systematically investigates how each type of resource, e.g., dictionary, grammar book, and retrieved parallel examples, affect the translation performance.<n>Our results indicate that high-quality dictionaries and good parallel examples are very helpful, while grammars hardly help.
  arXiv  Detail & Related papers  (2025-02-17T14:53:49Z)
• A Reality Check on Context Utilisation for Retrieval-Augmented Generation [44.54803681476863]
  We introduce DRUID (Dataset of Retrieved Unreliable, Insufficient and Difficult-to-understand contexts) with real-world queries and contexts manually annotated for stance.<n>The dataset is based on the task of automated claim verification, for which automated retrieval of real-world evidence is crucial.<n>We show that synthetic datasets exaggerate context characteristics rare in real retrieved data, which leads to inflated context utilisation results.
  arXiv  Detail & Related papers  (2024-12-22T14:16:38Z)
• Context-Parametric Inversion: Why Instruction Finetuning Can Worsen Context Reliance [68.56701216210617]
  In-principle, one would expect models to adapt to the user context better after instruction finetuning.<n>We observe a surprising failure mode: during instruction tuning, the context reliance under knowledge conflicts initially increases as expected, but then gradually decreases.
  arXiv  Detail & Related papers  (2024-10-14T17:57:09Z)
• Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples [13.946626388239443]
  We aim to improve sentence embeddings without using large manually annotated datasets. We will focus on automatic dataset generation through few-shot learning and explore the appropriate methods to leverage few-shot examples.
  arXiv  Detail & Related papers  (2024-02-23T06:33:51Z)
• Vocabulary-Defined Semantics: Latent Space Clustering for Improving In-Context Learning [32.178931149612644]
  In-context learning enables language models to adapt to downstream data or incorporate tasks by few samples as demonstrations within the prompts. However, the performance of in-context learning can be unstable depending on the quality, format, or order of demonstrations. We propose a novel approach "vocabulary-defined semantics"
  arXiv  Detail & Related papers  (2024-01-29T14:29:48Z)
• Pre-Training to Learn in Context [138.0745138788142]
  The ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. We propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters.
  arXiv  Detail & Related papers  (2023-05-16T03:38:06Z)
• Finding Support Examples for In-Context Learning [73.90376920653507]
  We propose LENS, a fiLter-thEN-Search method to tackle this challenge in two stages. First we filter the dataset to obtain informative in-context examples individually. Then we propose diversity-guided example search which iteratively refines and evaluates the selected example permutations.
  arXiv  Detail & Related papers  (2023-02-27T06:32:45Z)
• Compositional Exemplars for In-context Learning [21.961094715261133]
  Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability. We propose CEIL (Compositional Exemplars for In-context Learning) to model the interaction between the given input and in-context examples. We validate CEIL on 12 classification and generation datasets from 7 distinct NLP tasks, including sentiment analysis, paraphrase detection, natural language inference, commonsense reasoning, open-domain question answering, code generation, and semantic parsing.
  arXiv  Detail & Related papers  (2023-02-11T14:02:08Z)
• An Explanation of In-context Learning as Implicit Bayesian Inference [117.19809377740188]
  We study the role of the pretraining distribution on the emergence of in-context learning. We prove that in-context learning occurs implicitly via Bayesian inference of the latent concept. We empirically find that scaling model size improves in-context accuracy even when the pretraining loss is the same.
  arXiv  Detail & Related papers  (2021-11-03T09:12:33Z)
• Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning [30.5853328612593]
  In this work, we explore fine-tuning methods of BERT -- a pre-trained Transformer based language model. Our experimental results show an advantage in model performance by maximizing the approximate knowledge gain of the model. We analyze the benefits of freezing layers of the language model during fine-tuning to reduce the number of trainable parameters.
  arXiv  Detail & Related papers  (2020-12-04T08:34:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.