mlsanityData to 14 Oct 2026

UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models

arXiv 2312.11036 ↗First posted 18 Dec 2023cs.IR cs.AI cs.CLcited by 14

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Abstract (arXiv)

Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in the area of information retrieval and natural language processing. Existing methods for GDR and GAR rely on separate retrieval and reader modules, which hinder simultaneous optimization. To overcome this, we present UniGen, a Unified Generative framework for retrieval and question answering that integrates both tasks into a single generative model leveraging the capabilities of large language models. UniGen employs a shared encoder and two distinct decoders for generative retrieval and question answering. To facilitate the learning of both tasks, we introduce connectors, generated by large language models, to bridge the gaps between query inputs and generation targets, as well as between document identifiers and answers. Furthermore, we propose an iterative enhancement strategy that leverages generated answers and retrieved documents to iteratively improve both tasks. Through extensive experiments on the MS MARCO and NQ datasets, we demonstrate the effectiveness of UniGen, showcasing its superior performance in both the retrieval and the question answering tasks.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 32 of its 40 references that are in the map. First posted on arXiv 18 Dec 2023.

Builds on

32 of 40 references.

References · 32

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  7. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  8. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  9. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  10. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  11. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  12. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
20 more
  1. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  2. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  3. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 849
  4. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  5. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  6. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 298
  7. 2022Transformer Memory as a Differentiable Search Indexcited by 247
  8. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  9. 2021End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answeringcited by 133
  10. 2022A Neural Corpus Indexer for Document Retrievalcited by 111
  11. 2020Training Question Answering Models From Synthetic Datacited by 95
  12. 2023Learning to Tokenize for Generative Retrievalcited by 68
  13. 2022Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generationcited by 48
  14. 2023RETA-LLM: A Retrieval-Augmented Large Language Model Toolkitcited by 40
  15. 2022DSI++: Updating Transformer Memory with New Documentscited by 35
  16. 2022Ultron: An Ultimate Retriever on Corpus with a Model-based Indexercited by 31
  17. 2023Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategiescited by 25
  18. 2023TOME: A Two-stage Approach for Model-based Retrievalcited by 21
  19. 2023Generative Retrieval via Term Set Generationcited by 13
  20. 2022DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Indexcited by 12

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Citing papers per year: 2024: 8; 2025: 5; 2026: 1.

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  1. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 166
  2. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  3. 2024Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphscited by 78
  4. 2025Enhancing Decision-Making of Large Language Models via Actor-Criticcited by 7
  5. 2024AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistantcited by 5
  6. 2024Self-Retrieval: End-to-End Information Retrieval with One Large Language Modelcited by 4
  7. 2024A Scalable Data-Driven Framework for Systematic Analysis of SEC 10-K Filings Using Large Language Modelscited by 3
  8. 2024An Entailment Tree Generation Approach for Multimodal Multi-Hop Question Answering with Mixture-of-Experts and Iterative Feedback Mechanismcited by 3
  9. 2026Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphscited by 3
  10. 2025Generative Representational Learning of Foundation Models for Recommendationcited by 2
  11. 2025Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Modelscited by 2
  12. 2025TeamCMU at Touch'e: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Searchcited by 1
2 more
  1. 2024Fine-grained Stateful Knowledge Exploration: Effective and Efficient Graph Retrieval with Large Language Modelscited by 0
  2. 2025When & How to Write for Personalized Demand-aware Query Rewriting in Video Searchcited by 0