Papers with retrieval methods
Multi-Domain Dialogue State Tracking By Neural-Retrieval Augmentation (2022.findings-aacl)
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| Challenge: | Existing approaches for DST are conditioned on previous dialogue states, but the dependency on previous dialogs makes it difficult to prevent error propagation to subsequent turns. |
| Approach: | They propose to create a Neural Index based on dialogue context by analyzing user dialogue and previous turn state and generating a retrieval-guided generation approach. |
| Outcome: | The proposed framework retrieves dialogue context from the index built using unstructured dialogue state and structured user/system utterances. |
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering (2023.emnlp-industry)
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Fangkai Yang, Pu Zhao, Zezhong Wang, Lu Wang, Bo Qiao, Jue Zhang, Mohit Garg, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Large Language Models (LLMs) have gained popularity but lack specific domain knowledge in domain-specific tasks. |
| Approach: | They propose a model interaction paradigm that empowers LLM to achieve better performance on domain-specific tasks where it is not proficient. |
| Outcome: | The proposed approach outperforms the commonly used LLM with retrieval methods in domain-specific tasks. |
Enhancing Retrieval Systems with Inference-Time Logical Reasoning (2025.acl-short)
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| Challenge: | Existing retrieval methods rely on transforming user queries into vector representations and retrieving documents based on cosine similarity and static embeddings. |
| Approach: | They propose an inference-time logical reasoning framework that incorporates logical thinking into retrieval process. |
| Outcome: | The proposed method outperforms traditional retrieval methods on synthetic and real-world benchmarks on synthetic queries and datasets. |
SLARD: A Chinese Superior Legal Article Retrieval Dataset (2025.coling-main)
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| Challenge: | Existing retrieval methods struggle to achieve ideal results, a study finds . existing large language models lack prior knowledge of the content of superior legal articles . |
| Approach: | They propose to use a Chinese superior legal article retrieval dataset to find relevant articles with higher legal effectiveness. |
| Outcome: | The proposed dataset shows that existing retrieval methods struggle to achieve ideal results. |
Transform Retrieval for Textual Entailment in RAG (2025.naacl-short)
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| Challenge: | Existing retrieval methods prioritize relevance without ensuring the retrieved documents semantically support answering the queries. |
| Approach: | They propose a novel approach to improve Textual Entailment Retrieval within the framework of Retri-Augmented Generation (RAG) they transform query embeddings to better align with semantic entailment without re-encoding the document corpus. |
| Outcome: | The proposed approach consistently approaches the skyline across multiple datasets, demonstrating its strength in many-to-many retrieval scenarios. |
kNN Retrieval for Simple and Effective Zero-Shot Multi-speaker Text-to-Speech (2025.naacl-short)
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| Challenge: | Neural text-to-speech (TTS) models typically rely on extensive transcribed speech datasets and intricate training pipelines. |
| Approach: | They propose a framework for zero-shot multi-speaker text-to-speech using retrieval methods which leverage the linear relationships between SSL features. |
| Outcome: | The proposed framework achieves comparable performance to state-of-the-art models trained on large training datasets. |
Aligning Retrieval with Reader Needs: Reader-Centered Passage Selection for Open-Domain Question Answering (2025.coling-main)
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Chunlei Xin, Shuheng Zhou, Xuanang Chen, Yaojie Lu, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing retrieval methods aim to gather relevant passages but fail to prioritize consistent and useful information for the reader. |
| Approach: | They propose a novel method which re-ranks passages based on the reader's prediction probability distribution and clusters passage according to the predicted answers. |
| Outcome: | The proposed method improves the quality of evidence passages under zero-shot scenarios. |
An Empirical Comparison of Instance Attribution Methods for NLP (2021.naacl-main)
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| Challenge: | Influence functions provide machinery for identifying training instances that may have led to a specific prediction, but are computationally expensive and prohibitive in many cases. |
| Approach: | They evaluate the degree to which different potential instance attribution agrees with respect to the importance of training samples. |
| Outcome: | The proposed methods exhibit desirable characteristics similar to more complex methods, but are computationally expensive. |
Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval (2022.findings-emnlp)
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| Challenge: | a recent study has shown that dense retrieval methods are suboptimal for capturing contextual similarities in complex data. |
| Approach: | They propose to combine a structure search method and efficient bi-encoder dense retrieval models to capture contextual similarities. |
| Outcome: | The proposed model improves on token-level and passage-level dense retrieval tasks. |
DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation (2026.findings-eacl)
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| Challenge: | Retrieval-augmented generation (RAG) is a common technique for grounding language models in domain-specific information. |
| Approach: | They propose a new retrieval technique that incorporates diversity into the retrieval step to improve performance on reasoning-intensive QA benchmarks. |
| Outcome: | The proposed method outperforms baselines on reasoning-intensive QA benchmarks by 4–10%. |
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval (2025.findings-naacl)
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| Challenge: | Retrieval-Augmented Generation models fail to rank the most relevant documents at the top . conventional retrieval methods fail to find the most important documents . |
| Approach: | They propose a new method for scoring retrieved documents using zero-shot answer scent based on a pre-trained large language model to compute the likelihood of document-derived answers aligning with the answer scent. |
| Outcome: | The proposed method improves top-1 retrieval accuracy on NQ, TriviaQA, WebQA, ArchivalQA, HotpotQA, and Entity Questions. |
MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering (2025.findings-emnlp)
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| Challenge: | Existing methods for answering time-sensitive questions lack temporal reasoning . existing methods struggle with these time-intensive questions, authors say . |
| Approach: | They propose a temporal-based question-answering framework that integrates temporal perturbations and gold evidence labels into a question processing framework. |
| Outcome: | The proposed framework outperforms baseline retrieval methods in retrieval performance. |
Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for retrieval augmentation work with chunked contexts, which leads to poor quality of semantic representation and incomplete retrieval of useful information. |
| Approach: | They propose a method for retrieval augmentation of long-context language modeling using landmark embedding. |
| Outcome: | The proposed method outperforms existing retrieval methods with a notable advantage. |
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization (2024.findings-naacl)
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| Challenge: | Existing methods to generate source code summaries are coarse-grained and noise-filled . however, they do not capture contextual code semantics and are often outdated in continuous software iteration. |
| Approach: | They propose a fine-grained Token-level retrieval-augmented mechanism on the decoder side to enhance performance of neural models. |
| Outcome: | The proposed method produces more low-frequency tokens and is interpretable. |
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison (2024.findings-naacl)
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| Challenge: | Existing approaches to extract examples from memory are limited, but the upstream retrieval step is still unexplored. |
| Approach: | They propose to use a standard autoregressive model, edit-based model and a large language model with in-context learning to investigate the effect of retrieval methods on translation scores. |
| Outcome: | The proposed architectures improve translation scores and increase diversity of examples. |
A Multi-Task Embedder For Retrieval Augmented LLMs (2024.acl-long)
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| Challenge: | Existing retrieval methods face limitations in terms of knowledge, memory, and action. |
| Approach: | They propose a retrieval enhancement mechanism that brings in useful information from external sources to augment the LLM. |
| Outcome: | The proposed method significantly improves the LLM’s performance in various downstream tasks while introducing superior retrieval augmentation’s effect over both general and task-specifc retrievers. |
A Zero-Shot Monolingual Dual Stage Information Retrieval System for Spanish Biomedical Systematic Literature Reviews (2024.naacl-long)
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| Challenge: | Existing studies have shown that most SRs are skewed towards English databases, excluding databases in Languages other than English (LoE). |
| Approach: | They propose a zero-shot dual information retrieval baseline system that integrates traditional retrieval methods with pre-trained language models and cross-attention re-rankers for enhanced accuracy in Spanish biomedical literature retrieval. |
| Outcome: | The proposed system improves on three real-life case studies in Spanish biomedical literature retrieval using the LILACS database, which is known for its coverage of Latin American and Caribbean biomedically literature. |
RepoDistill: Distilling Repository Knowledge through Compression-Aware Budget Allocation and Policy Optimization (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have strong performance on code translation tasks, but they struggle with repository-level scenarios where context is extensive and interdependent. |
| Approach: | They propose a framework that integrates retrieval with learning budget allocation for fine-grained context compression. |
| Outcome: | The proposed framework outperforms baselines on SWE-QA, CoderEval, and LongCodeU. |
Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline (2025.findings-emnlp)
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| Challenge: | Existing retrieval methods neglect the execution sequence structures inherent in procedural documents. |
| Approach: | They propose a retrieval model which integrates procedural graphs with document representations. |
| Outcome: | The proposed model integrates procedural graphs with document representations to improve document retrieval. |
VIMI: Grounding Video Generation through Multi-modal Instruction (2024.emnlp-main)
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Yuwei Fang, Willi Menapace, Aliaksandr Siarohin, Tsai-Shien Chen, Kuan-Chieh Wang, Ivan Skorokhodov, Graham Neubig, Sergey Tulyakov
| Challenge: | Existing text-to-video diffusion models rely on text-only encoders for their pretraining, restricting their versatility and application in multimodal integration. |
| Approach: | They propose a multimodal conditional video generation framework for pretraining on augmented text prompts and then utilize a two-stage training strategy to enable diverse video generation tasks within a model. |
| Outcome: | The proposed model can synthesize consistent and temporally coherent videos with large motion while retaining the semantic control. |
Retrieval Enhanced Model for Commonsense Generation (2021.findings-acl)
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| Challenge: | Existing frameworks for commonsense generation are lacking for pre-trained models. |
| Approach: | They propose a framework that uses concept matching to retrieve prototype sentences and trainable sentence retriever to enhance pre-training and fine-tuning. |
| Outcome: | The proposed framework achieves state-of-the-art on the large-scale Common-Gen benchmark. |
Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Aspect-based sentiment analysis (ABSA) identifies sentiment information related to specific aspects . previous studies have proposed using fixed examples for instruction tuning . |
| Approach: | They propose an instruction learning method with retrieval-based example ranking for ABSA tasks. |
| Outcome: | The proposed method is superior to existing models on three ABSA subtasks. |
Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain (2025.coling-main)
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| Challenge: | Existing research focuses on a limited set of retrieval methods, evaluated in pairs on domain-general datasets exclusively in English. |
| Approach: | They evaluate the efficacy of hybrid search across a variety of retrieval models in the french language . they find that fusion of different domain-general models consistently enhances performance . |
| Outcome: | The proposed model improves in-domain performance compared to a single model in a zero-shot context . the proposed model also improves when the models are trained in- domain . |
Generation-Augmented Retrieval for Open-Domain Question Answering (2021.acl-long)
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| Challenge: | Existing approaches to answer open-domain questions use sparse representations and sparsity. |
| Approach: | They propose a method which augments a query by generating relevant contexts from heuristically discovered contexts without external supervision. |
| Outcome: | The proposed approach outperforms state-of-the-art dense retrieval methods on natural questions and triviaQA datasets. |
RASD: Retrieval-Augmented Speculative Decoding (2025.findings-acl)
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| Challenge: | Existing methods for generating draft tokens rely on lightweight draft models or additional model structures to generate tokens and retrieve context from databases. |
| Approach: | They propose to use a pruning method to enhance model-based speculative decoding by combining the best-fit model with the best retrieval tree. |
| Outcome: | The proposed method achieves state-of-the-art inference acceleration across tasks such as DocQA, Summary, Code, and In-Domain QA. |
Graph Neural Network Enhanced Retrieval for Question Answering of Large Language Models (2025.naacl-long)
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| Challenge: | Existing retrieval methods divide reference documents into passages, treating them in isolation. Existing methods only use contiguous passages or keywords. |
| Approach: | They propose a retrieval method that leverages graph neural networks to exploit relatedness between passages to enhance retrieval. |
| Outcome: | The proposed method improves retrieval by exploiting the relatedness between passages. |
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering (2022.acl-long)
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| Challenge: | Existing retrieval methods for knowledge base question answering are either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs. |
| Approach: | They propose a subgraph retrieval framework that decouples the retrieval from the subsequent reasoning process and trains subgraphs for easier reasoning. |
| Outcome: | The proposed framework improves retrieval and QA performance over existing methods. |
Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty (2023.findings-emnlp)
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| Challenge: | Retrieval augmentation is effective for large graph parsing tasks, but can fail to identify the most informative exemplars . structure-aware and uncertainty-guided adaptive retrieval (SUGAR) exploits two unique sources of information: structural similarity and model uncertainty. |
| Approach: | They propose a structure-aware and uncertainty-guided adaptive retrieval approach that exploits structural similarity and model uncertainty to improve retrieval-augmented parsing for complex graph problems. |
| Outcome: | The proposed method improves retrieval-augmented parsing for graph parsers with large output graphs and non-trivial structure. |
Toward Multi-Session Personalized Conversation: A Large-Scale Dataset and Hierarchical Tree Framework for Implicit Reasoning (2025.emnlp-main)
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| Challenge: | Existing long-term open-domain dialogue datasets lack complex, real-world personalization and fail to capture implicit reasoning. |
| Approach: | They propose a large-scale long-term dataset with 2,500 examples containing approximately 100 conversation sessions to study implicit reasoning in personalized dialogues. |
| Outcome: | The proposed model improves the ability of LLMs to reason over long-term conversations with implicit contextual dependencies. |
UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever (2025.acl-long)
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Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, Kun Kuang
| Challenge: | Existing retrieval methods are designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types. |
| Approach: | They propose a novel retrieval method that integrates specialized knowledge into LLMs. |
| Outcome: | The proposed method can perform multiple legal retrieval tasks for LLMs. |
SAM Decoding: Speculative Decoding via Suffix Automaton (2025.acl-long)
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| Challenge: | Speculative decoding (SD) methods are inefficient and rely on single retrieval resources. |
| Approach: | They propose a retrieval-based speculative decoding method that adapts the suffix automaton for efficient draft generation by utilizing the generating text sequence and static text corpus. |
| Outcome: | The proposed method can find the longest suffix match and can be integrated with existing methods to generalize to broader domains. |
A Quality-based Syntactic Template Retriever for Syntactically-Controlled Paraphrase Generation (2023.emnlp-main)
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| Challenge: | Existing syntactically-controlled paraphrase generation models perform well with human-annotated or well-chosen syntaktic templates. |
| Approach: | They propose a quality-based Syntactic Template Retriever to retrieve templates based on the quality of the to-be-generated paraphrases. |
| Outcome: | The proposed algorithm can generate high-quality paraphrases without sacrificing quality. |
Detrimental Contexts in Open-Domain Question Answering (2023.findings-emnlp)
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| Challenge: | Using the whole passages in QA datasets can improve model accuracy by 10% . |
| Approach: | They analyze how passages can have a detrimental effect on retrieve-then-read architectures used in question answering when evaluated on common question answering datasets. |
| Outcome: | The proposed model accuracy can be improved by 10% on two popular QA datasets by filtering out detrimental passages. |
Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases (2025.findings-acl)
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Yongjia Lei, Haoyu Han, Ryan A. Rossi, Franck Dernoncourt, Nedim Lipka, Mahantesh M Halappanavar, Jiliang Tang, Yu Wang
| Challenge: | Existing methods for textual and structural retrieval ignore mutual reinforcement and only use structural retrievals for text-rich Graph Knowledge Bases (TG-KBs). |
| Approach: | They propose a Mixture of Structural-and-Textual Retrieval to retrieve textual and structural knowledge via a Planning-Reasoning-Organizing framework. |
| Outcome: | Experiments show that the proposed framework performs better than existing methods in analyzing TG-KBs and integrating structural trajectories for candidate reranking. |
MAGIC: Multi-Argument Generation with Self-Refinement for Domain Generalization in Automatic Fact-Checking (2024.lrec-main)
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| Challenge: | Existing methods for fact-checking are limited in retrieving evidence from documents . retrieved evidence derived from different sources strains generalization capabilities of classifiers . |
| Approach: | They propose a framework for cross-domain fact-checking using multi-argument generation . they propose to reconstruct concise evidence from large amounts of evidence retrieved from different sources . |
| Outcome: | The proposed framework is effective in identifying the veracity of out-of-domain claims . it can be used to extract evidence from documents and verify claims across domains . |
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering (2025.acl-long)
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| Challenge: | Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new heights. |
| Approach: | They propose an embedding-level framework that enhances both the retriever and the reader by reordering query representations via lightweight linear layers under an unsupervised contrastive learning objective. |
| Outcome: | The proposed framework outperforms baselines in accuracy and efficiency across three open-source LLMs, three retrieval methods, and four ODQA benchmarks. |
SynapticRAG: Enhancing Temporal Memory Retrieval in Large Language Models through Synaptic Mechanisms (2025.findings-acl)
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| Challenge: | Existing retrieval methods in Large Language Models show degradation in accuracy when handling temporally distributed conversations. |
| Approach: | They propose a method that combines temporal triggers and synaptic-like stimulus propagation to identify relevant dialogue histories. |
| Outcome: | The proposed approach improves on four datasets of English, Chinese and Japanese compared to state-of-the-art retrieval methods by 14.66% points. |
Controlled Retrieval-augmented Context Evaluation for Long-form RAG (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) enhances large language models by incorporating context retrieved from external knowledge sources. |
| Approach: | They propose a Controlled Retrieval-aUgmented conteXt evaluation framework to directly assess retrieval-augmented contexts. |
| Outcome: | The proposed framework uses human-written summaries to control the information scope of knowledge. |
Dynamic Tool Dependency Retrieval for Lightweight Function Calling (2026.findings-acl)
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| Challenge: | Existing retrieval methods rely on static inputs, failing to capture multi-step tool dependencies and evolving task context. |
| Approach: | They propose a lightweight retrieval method that conditions on initial query and evolving task context. |
| Outcome: | The proposed method improves function calling success rates between 23% and 104% compared to state-of-the-art retrieval methods. |
SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering (2026.findings-acl)
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FU Yuqing, Yimin Deng, Wanyu Wang, Yuhao Wang, Yejing Wang, Hongshi Liu, Yiqi Wang, Xiao Han, Maolin Wang, Guoshuai Zhao, Yi Chang, Xiangyu Zhao
| Challenge: | Existing approaches to multi-hop question answering lack effective control over reasoning paths, leading to astray results. |
| Approach: | They propose a framework for multi-hop question answering that trains an end-to-end reasoning path navigator to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model. |
| Outcome: | The proposed framework trains an end-to-end reasoning path navigator . it is able to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model . |