mlsanityData to 7 Oct 2026

Retrieval Augmented Instruction Tuning for Open NER with Large Language Models

arXiv 2406.17305 ↗First posted 25 Jun 2024cs.CLcited by 4

On the map: Retrieval-augmented generation → Generation and context integration

Abstract (arXiv)

The strong capability of large language models (LLMs) has been applied to information extraction (IE) through either retrieval augmented prompting or instruction tuning (IT). However, the best way to incorporate information with LLMs for IE remains an open question. In this paper, we explore Retrieval Augmented Instruction Tuning (RA-IT) for IE, focusing on the task of open named entity recognition (NER). Specifically, for each training sample, we retrieve semantically similar examples from the training dataset as the context and prepend them to the input of the original instruction. To evaluate our RA-IT approach more thoroughly, we construct a Chinese IT dataset for open NER and evaluate RA-IT in both English and Chinese scenarios. Experimental results verify the effectiveness of RA-IT across various data sizes and in both English and Chinese scenarios. We also conduct thorough studies to explore the impacts of various retrieval strategies in the proposed RA-IT framework. Code and data are available at: https://github.com/Emma1066/Retrieval-Augmented-IT-OpenNER

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation. Builds on 20 of its 34 references that are in the map. First posted on arXiv 25 Jun 2024.

Builds on

20 of 34 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023RA-DIT: Retrieval-Augmented Dual Instruction Tuning2 Oct
  2. 2023In-Context Retrieval-Augmented Language Models31 Jan

Other references · 18

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  3. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  4. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 258
  5. 2023InstructUIE: Multi-task Instruction Tuning for Unified Information Extractioncited by 167
  6. 2023GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extractioncited by 116
  7. 2023Skywork: A More Open Bilingual Foundation Modelcited by 110
  8. 2023MoDS: Model-oriented Data Selection for Instruction Tuningcited by 107
  9. 2020CrossNER: Evaluating Cross-Domain Named Entity Recognitioncited by 102
  10. 2021CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmarkcited by 99
  11. 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAGcited by 90
  12. 2023Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extractioncited by 89
6 more
  1. 2023LLMaAA: Making Large Language Models as Active Annotatorscited by 78
  2. 2023Empirical Study of Zero-Shot NER with ChatGPTcited by 60
  3. 2024Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimationcited by 53
  4. 2023Retrieval-Augmented Code Generation for Universal Information Extractioncited by 40
  5. 2024KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extractioncited by 33
  6. 2020CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinesecited by 19

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTunecited by 1
  2. 2025ToMMeR -- Efficient Entity Mention Detection from Large Language Modelscited by 0
  3. 2026Instruction Finetuning LLaMA-3-8B Model Using LoRA for Financial Named Entity Recognitioncited by 0
  4. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning
  2. 2024Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model
  3. 2023DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition
  4. 2026URA-NER: A Unified Retrieval-Augmented Framework with Retrieval Alignment and Uncertainty Reduction for Low-Resource NER
  5. 2022kNN-NER: Named Entity Recognition with Nearest Neighbor Search
  6. 2024RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
  7. 2025Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms
  8. 2024Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation