Challenge: Generative retrieval (GR) models can be expensive and brittle out of domain.
Approach: They propose a query specification for gEnerative Keyword-Based Retrieval which bridges GR and query reformulation by learning to generate explicit keyword-based search specifications.
Outcome: The proposed query specification improves over existing queries and maintains strong efficiency.

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Reinforced Query Reasoners for Reasoning-intensive Retrieval Tasks (2025.emnlp-main)

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Challenge: Existing methods for retrieval of information excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks.
Approach: They propose a family of small-scale language models for query reasoning and rewriting in reasoning-intensive retrieval.
Outcome: The proposed model outperforms existing models on a BRIGHT benchmark with BM25 retrievers.
Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback (2023.emnlp-main)

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Challenge: End-to-end generative retrieval models produce document identifiers in response to a query . however, this approach has two challenges: an overemphasis on top-1 results at the expense of overall ranking quality.
Approach: They propose a generative retrieval model with reinforcement learning from relevance feedback to align token-level docid generation with document-level relevance estimation.
Outcome: The proposed model aligns token-level docid generation with document-level relevance estimation.
Expand, Highlight, Generate: RL-driven Document Generation for Passage Reranking (2023.emnlp-main)

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Challenge: Existing studies use large language models to generate training data for ranking models.
Approach: They propose a pipeline that generates synthetic documents from queries using large language models . they propose RL-based reinforcement learning to optimize the pipeline .
Outcome: The proposed pipeline outperforms existing state-of-the-art methods in generating synthetic documents more effectively.
Disentangling Questions from Query Generation for Task-Adaptive Retrieval (2024.findings-emnlp)

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Challenge: Existing work generates synthetic queries from domain-specific documents to jointly train the retriever.
Approach: They propose a query generator that better adapts to wide search intents expressed in the BeIR benchmark.
Outcome: The proposed query generator outperforms baselines and existing models on tasks with underexplored intents while using a query generator 47 times smaller than the previous state-of-the-art.
DiffusionRet: Diffusion-Enhanced Generative Retriever using Constrained Decoding (2023.findings-emnlp)

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Challenge: Generative retrieval methods have suffered from the lack of the intermediate reasoning step . generative retrieval uses sequence-to-sequence diffusion models to map a query to relevant docids .
Approach: They propose a novel method that uses query as an intermediate step before retrieval . they propose to use sequence-to-sequence diffusion models to map a query to relevant docids .
Outcome: Experiments show that proposed method outperforms existing methods on MARCO and Natural Questions datasets.
Query Rewriting in Retrieval-Augmented Large Language Models (2023.emnlp-main)

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Challenge: Existing studies focus on adapting either the retriever or the reader, but this approach is more focused on adaptation of the query itself.
Approach: They propose a new framework for retrieval-augmented Large Language Models . they propose rewrite-retrieve-read instead of retrieve-then-read .
Outcome: The proposed framework improves performance on downstream tasks, open-domain QA and multiple-choice QA.
Relevance-assisted Generation for Robust Zero-shot Retrieval (2023.emnlp-industry)

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Challenge: Despite strong in-domain performance, dense retrievers have shown poor generalization to out-of-domain zero-shot tasks where no training queries are available.
Approach: They propose to generate domain-specific pseudo queries for fine-tuning with domain-relevant relevance between PQ and documents.
Outcome: The proposed approach is more robust to domain shifts, validated on BEIR zero-shot tasks.
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy (2023.findings-emnlp)

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Challenge: Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation.
Approach: They propose to have large language models actively involved in retrieval to guide retrieval with generation.
Outcome: The proposed method synergizes retrieval and generation in an iterative manner, and can generate better results in subsequent iterations.
CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval (2025.findings-acl)

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Challenge: Recent techniques such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieleval (GDR) leverage LLMs to enhance retrieval performance but face key challenges: GAR’s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLM.
Approach: They propose a Context-Aware Generation-Augmented Retrieval approach which integrates corpus information into their generation process.
Outcome: Experimental results show that CA-GAR outperforms existing methods on seven tasks and four non-English languages.
tRAG: Term-level Retrieval-Augmented Generation for Domain-Adaptive Retrieval (2025.naacl-long)

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Challenge: Neural retrieval models suffer when there is a domain shift between training and test data distributions.
Approach: They propose to generate domain-adapted pseudo-queries using large language models (LLMs) to improve term recall of unseen query terms by using term-level Retrieval-Augmented Generation (tRAG).
Outcome: The proposed method significantly improves recall for unseen terms by 10.6% and outperforms LLM and retrieval-augmented generation baselines on overall retrieval performance.

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