LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition

• URL: http://arxiv.org/abs/2505.23722v2
• Date: Wed, 29 Oct 2025 17:27:45 GMT
• Title: LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition
• Authors: Fan Bai, Hamid Hassanzadeh, Ardavan Saeedi, Mark Dredze,
• Abstract summary: In-context learning (ICL) enables large language models to perform new tasks using only a few demonstrations.<n>Existing ICL methods typically rely on task-agnostic semantic similarity for demonstration retrieval.<n>We introduce DEER, a training-free ICL approach that enables LLMs to make more informed entity predictions.
• Score: 10.920384665824807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-context learning (ICL) enables large language models (LLMs) to perform new tasks using only a few demonstrations. However, in Named Entity Recognition (NER), existing ICL methods typically rely on task-agnostic semantic similarity for demonstration retrieval, which often yields less relevant examples and leads to inferior results. We introduce DEER, a training-free ICL approach that enables LLMs to make more informed entity predictions through the use of label-grounded statistics. DEER leverages token-level statistics from training labels to identify tokens most informative for entity recognition, enabling entity-focused demonstrations. It further uses these statistics to detect and refine error-prone tokens through a targeted reflection step. Evaluated on five NER datasets across four LLMs, DEER consistently outperforms existing ICL methods and achieves performance comparable to supervised fine-tuning. Further analyses demonstrate that DEER improves example retrieval, remains effective on both seen and unseen entities, and exhibits strong robustness in low-resource settings.

Related papers

• Leveraging In-Context Learning for Language Model Agents [51.2996117207114]
  In-context learning (ICL) with dynamically selected demonstrations combines the flexibility of prompting large language models (LLMs) with the ability to leverage training data to improve performance.<n>We show that set-selection of trajectories of similar tasks as demonstrations significantly improves performance, reliability, robustness, and efficiency of LLM agents.<n>We find that demonstrations obtained from larger models (in the annotation phase) also improve smaller models, and that ICL agents can even rival costlier trained agents.
  arXiv  Detail & Related papers  (2025-06-16T05:37:49Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning [53.02571749383208]
  In-Context Learning (ICL) empowers Large Language Models (LLMs) to tackle diverse tasks by incorporating multiple input-output examples.<n>Many-Shot Adaptive Pseudo-LabEling (MAPLE) is a novel influence-based many-shot ICL framework that utilizes pseudo-labeled samples to compensate for the lack of label information.
  arXiv  Detail & Related papers  (2025-05-22T04:54:27Z)
• PICLe: Pseudo-Annotations for In-Context Learning in Low-Resource Named Entity Detection [56.916656013563355]
  In-context learning (ICL) enables Large Language Models to perform tasks using few demonstrations.<n>We propose PICLe, a framework for in-context learning with noisy, pseudo-annotated demonstrations.<n>We evaluate PICLe on five biomedical NED datasets and show that, with zero human annotation, PICLe outperforms ICL in low-resource settings.
  arXiv  Detail & Related papers  (2024-12-16T16:09:35Z)
• CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition [3.695767900907561]
  CLLMFS is a Contrastive Learning enhanced Large Language Model framework for Few-Shot Named Entity Recognition. It integrates Low-Rank Adaptation (LoRA) and contrastive learning mechanisms specifically tailored for few-shot NER. Our method has achieved state-of-the-art performance improvements on F1-score ranging from 2.58% to 97.74% over existing best-performing methods.
  arXiv  Detail & Related papers  (2024-08-23T04:44:05Z)
• Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context Learning [0.0]
  We introduce logit separability, a criterion to assess the clarity of both samples and class-related words at the logit level. We find that incorporating multiple class-related words for each sample, rather than relying on a single class name, improves performance by offering a broader range of label information. We propose LICL, a logit separability-based method that jointly organizes samples and integrates multiple class-related words into each sample-label pair.
  arXiv  Detail & Related papers  (2024-06-16T12:11:46Z)
• Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning [41.606494950216764]
  In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs) This paper decomposes the overall performance of ICL into three dimensions, label space, format, and discrimination. We show that ICL exhibits significant efficacy in regulating the label space and format, which helps LLMs respond to desired label words.
  arXiv  Detail & Related papers  (2024-04-11T08:20:10Z)
• Rectifying Demonstration Shortcut in In-Context Learning [15.08431909212102]
  Large language models (LLMs) are able to solve various tasks with only a few demonstrations utilizing their in-context learning (ICL) abilities. LLMs often rely on their pre-trained semantic priors of demonstrations rather than on the input-label relationships to proceed with ICL prediction.
  arXiv  Detail & Related papers  (2024-03-14T15:30:14Z)
• C-ICL: Contrastive In-context Learning for Information Extraction [54.39470114243744]
  c-ICL is a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods.
  arXiv  Detail & Related papers  (2024-02-17T11:28:08Z)
• In-Context Learning for Few-Shot Nested Named Entity Recognition [53.55310639969833]
  We introduce an effective and innovative ICL framework for the setting of few-shot nested NER. We improve the ICL prompt by devising a novel example demonstration selection mechanism, EnDe retriever. In EnDe retriever, we employ contrastive learning to perform three types of representation learning, in terms of semantic similarity, boundary similarity, and label similarity.
  arXiv  Detail & Related papers  (2024-02-02T06:57:53Z)
• Identifying and Analyzing Performance-Critical Tokens in Large Language Models [52.404072802235234]
  We study how large language models learn to perform tasks from demonstrations.<n>Our work sheds light on how large language models learn to perform tasks from demonstrations and deepens our understanding of the roles different types of tokens play in large language models.
  arXiv  Detail & Related papers  (2024-01-20T20:55:21Z)
• Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning [23.932500424117244]
  In-Context Learning (ICL) is an emergent capability of Large Language Models (LLMs) Previous studies have shown that using LLMs' outputs as labels is effective in training models to select demonstrations. This paper presents an analysis on different utility functions by focusing on LLMs' output probability given ground-truth output.
  arXiv  Detail & Related papers  (2023-11-16T07:03:54Z)
• Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models [16.16724411695959]
  This work pushes the performance boundary of zero-shot NER with powerful large language models (LLMs) We propose a training-free self-improving framework, which utilizes an unlabeled corpus to stimulate the self-learning ability of LLMs. Experiments on four benchmarks show substantial performance improvements achieved by our framework.
  arXiv  Detail & Related papers  (2023-11-15T12:47:52Z)
• Channel-Wise Contrastive Learning for Learning with Noisy Labels [60.46434734808148]
  We introduce channel-wise contrastive learning (CWCL) to distinguish authentic label information from noise. Unlike conventional instance-wise contrastive learning (IWCL), CWCL tends to yield more nuanced and resilient features aligned with the authentic labels. Our strategy is twofold: firstly, using CWCL to extract pertinent features to identify cleanly labeled samples, and secondly, progressively fine-tuning using these samples.
  arXiv  Detail & Related papers  (2023-08-14T06:04:50Z)
• IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations [11.008412414253662]
  Large Language Models (LLMs) encode meanings of words in the form of distributed semantics. Recent studies have shown that LLMs tend to generate unintended, inconsistent, or wrong texts as outputs. We propose a novel ensemble learning method, Interpretable Ensemble Representation Learning (IERL), that systematically combines LLM and crowdsourced knowledge representations.
  arXiv  Detail & Related papers  (2023-06-24T05:02:34Z)
• ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction [56.790794611002106]
  Large language models (LLMs) have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning. We propose a simple but effective in-context learning framework called ICL-D3IE. Specifically, we extract the most difficult and distinct segments from hard training documents as hard demonstrations.
  arXiv  Detail & Related papers  (2023-03-09T06:24:50Z)
• Disambiguation of Company names via Deep Recurrent Networks [101.90357454833845]
  We propose a Siamese LSTM Network approach to extract -- via supervised learning -- an embedding of company name strings. We analyse how an Active Learning approach to prioritise the samples to be labelled leads to a more efficient overall learning pipeline.
  arXiv  Detail & Related papers  (2023-03-07T15:07:57Z)
• Focusing on Potential Named Entities During Active Label Acquisition [0.0]
  Named entity recognition (NER) aims to identify mentions of named entities in an unstructured text. Many domain-specific NER applications still call for a substantial amount of labeled data. We propose a better data-driven normalization approach to penalize sentences that are too long or too short.
  arXiv  Detail & Related papers  (2021-11-06T09:04:16Z)
• Dash: Semi-Supervised Learning with Dynamic Thresholding [72.74339790209531]
  We propose a semi-supervised learning (SSL) approach that uses unlabeled examples to train models. Our proposed approach, Dash, enjoys its adaptivity in terms of unlabeled data selection.
  arXiv  Detail & Related papers  (2021-09-01T23:52:29Z)

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.