Papers with retrieval
LAVIS: A One-stop Library for Language-Vision Intelligence (2023.acl-demo)
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| Challenge: | a new open-source library for language-vision research and applications is available for free. |
| Approach: | They introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. |
| Outcome: | The proposed library is open-source and highly extensible and configurable. |
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems (2025.coling-industry)
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Rafael Teixeira de Lima, Shubham Gupta, Cesar Berrospi Ramis, Lokesh Mishra, Michele Dolfi, Peter Staar, Panagiotis Vagenas
| Challenge: | Retrieval Augmented Generation (RAG) systems are widespread in the industry. |
| Approach: | They propose to use Q&A datasets to assess retrieval performance and label-targeted data generation to refine RAG datasets. |
| Outcome: | The proposed system can generate Q&A datasets with fine-tuned small LLMs. |
RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models (2023.emnlp-demo)
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Yasuto Hoshi, Daisuke Miyashita, Youyang Ng, Kento Tatsuno, Yasuhiro Morioka, Osamu Torii, Jun Deguchi
| Challenge: | Existing libraries for building R-LLMs provide high-level abstractions without sufficient transparency for evaluating and optimizing prompts within specific inference processes. |
| Approach: | They propose an open-source framework to facilitate the development, evaluation, and optimization of R-LLMs for knowledge-intensive tasks. |
| Outcome: | The framework improves hand-crafted prompts, inference processes and quantitatively measures overall system performance. |
Wikipedia as a Resource for Text Analysis and Retrieval (P19-4)
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| Challenge: | Tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval. |
| Approach: | tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval. |
| Outcome: | This tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval. |
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development (2023.acl-demo)
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Avi Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Juergen Bross, Radu Florian, Salim Roukos
| Challenge: | Question Answering (QA) is a major area of research in Natural Language Processing (NLP) |
| Approach: | They propose a one-stop and open-source QA repository for question answering . it supports core QA functionalities like retrieval and reading comprehension . they say it will facilitate easy replication of state-of-the-art (SOTA) QA methods . |
| Outcome: | The proposed framework enables easy replication of state-of-the-art (SOTA) QA methods. |
CovRelex-SE: Adding Semantic Information for Relation Search via Sequence Embedding (2023.eacl-demo)
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| Challenge: | COVID-19 has affected all aspects of human life, causing problems related to acronyms, synonyms, and rare keywords. |
| Approach: | They propose a hybrid relation retrieval system based on embeddings to provide high-quality search results. |
| Outcome: | The proposed system can be accessed through the following URL: http://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/. |
Doc-React: Multi-page Heterogeneous Document Question-answering (2025.acl-short)
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Junda Wu, Yu Xia, Tong Yu, Xiang Chen, Sai Sree Harsha, Akash V Maharaj, Ruiyi Zhang, Victor Bursztyn, Sungchul Kim, Ryan A. Rossi, Julian McAuley, Yunyao Li, Ritwik Sinha
| Challenge: | Existing methods for integrating information across multiple modalities are suboptimal for multi-page, multimodal documents. |
| Approach: | They propose an adaptive iterative framework that balances information gain and uncertainty reduction at each step. |
| Outcome: | The proposed framework captures relevant multimodal content and achieves strong performance on complex QA tasks. |
Fast Exact Retrieval for Nearest-neighbor Lookup (FERN) (2024.naacl-srw)
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| Challenge: | Exact nearest neighbor search is computationally intensive and complex . Attention has shifted towards Approximate Nearest-Neighbor (ANN) retrieval techniques . |
| Approach: | They propose an algorithm for logarithmic Fast Exact Retrieval for Nearest-neighbor lookup which achieves O(dlog N) look-up with 100% recall on 10 million d=128 uniformly generated vectors. |
| Outcome: | The proposed algorithm achieves O(dlog N) look-up with 100% recall on 10 million d=128 uniformly generated vectors. |
Retrieval-based Language Models and Applications (2023.acl-tutorials)
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| Challenge: | In this tutorial, we will provide a comprehensive overview of retrieval-based language models. |
| Approach: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
| Outcome: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
T2-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation (2026.eacl-long)
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| Challenge: | Existing QA datasets containing text-and-table data typically contain context-dependent questions, which may yield multiple correct answers depending on the provided context. |
| Approach: | They propose a benchmark to evaluate RAG methods on text-and-table data. |
| Outcome: | The proposed method evaluates RAG methods on real-world text-and-table data. |
Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation (2024.findings-naacl)
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| Challenge: | Existing methods for length extrapolation are tailored for natural language modeling, a task known to have strong recency bias. |
| Approach: | They propose two attention alignment strategies to improve T5's long-context utilization capability without fine-tuning. |
| Outcome: | The proposed methods improve the long-context utilization capability of T5 on language modeling, retrieval, multi-document question answering, and code completion tasks without any fine-tuning. |
H-MEM: Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents (2026.eacl-long)
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| Challenge: | Long-term memory is one of the key factors influencing the reasoning capabilities of Large Language Model Agents. |
| Approach: | They propose a hierarchical memory architecture that organizes and updates memory in a multi-level fashion based on the degree of semantic abstraction. |
| Outcome: | The proposed model outperforms baseline methods on five task settings from the LoCoMo dataset. |
Context-Efficient Retrieval with Factual Decomposition (2025.naacl-short)
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| Challenge: | Existing models that use dynamically expanding text can be incorporated into large language models. |
| Approach: | They show that pre-processing external corpus into semi-structured "atomic facts" reduces the size of the context and improves inference efficiency. |
| Outcome: | The proposed form of atomic facts improves on question answering tasks when the amount of retrieved text is limited. |
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering (D19-58)
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Rajarshi Das, Ameya Godbole, Dilip Kavarthapu, Zhiyu Gong, Abhishek Singhal, Mo Yu, Xiaoxiao Guo, Tian Gao, Hamed Zamani, Manzil Zaheer, Andrew McCallum
| Challenge: | Multi-hop question answering (QA) requires an information retrieval system that can find multiple supporting evidence needed to answer the question. |
| Approach: | They propose a technique that uses information of entities present in the initial retrieved evidence to learn to ‘hop’ onto other relevant evidence. |
| Outcome: | The proposed method boosts retrieval performance on a multi-hop question answering dataset with 5 million Wikipedia paragraphs and a model without training increases its performance by 10.59 F1. |
KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education? (2025.acl-short)
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| Challenge: | Existing knowledge discrepancies between textbooks and large language models can undermine RAG systems' performance. |
| Approach: | They propose to use a dataset to test RAG system robustness against knowledge discrepancies. |
| Outcome: | The proposed dataset shows that RAG systems suffer performance degradation when faced with knowledge discrepancies. |
Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (2023.acl-long)
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| Challenge: | Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks. |
| Approach: | They investigate retrieval settings from the input and label distribution views . they further augment document-level EAE with pseudo demonstrations sampled from event semantic regions . |
| Outcome: | The proposed methods can augment document-level EAE with pseudo demonstrations . the methods can be used in generative NLP tasks such as dialogue response generation . |
DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information (2024.acl-demos)
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| Challenge: | Drug safety research is crucial for maintaining public health, but resources available to the public are limited. |
| Approach: | They propose an easy-to-use and interactive multi-source information visualisation platform for drug safety study. |
| Outcome: | The proposed platform provides a one-stop information analysis, retrieval, and annotation service. |
Dense Hierarchical Retrieval for Open-domain Question Answering (2021.findings-emnlp)
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| Challenge: | Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA) current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result. |
| Approach: | They propose a hierarchical framework which can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic specific to each passage. |
| Outcome: | The proposed framework significantly outperforms the original dense passage retriever and helps an end-to-end QA system outperfect the strong baselines on multiple open-domain QA benchmarks. |
Sparse, Dense, and Attentional Representations for Text Retrieval (2021.tacl-1)
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| Challenge: | Dual encoders perform retrieval by encoding documents and queries into dense low-dimensional vectors, scoring each document by its inner product with the query. |
| Approach: | They propose a dual-encoder-based neural model that combines the efficiency of dual encoders with expressiveness of more costly attentional architectures. |
| Outcome: | The proposed model outperforms strong alternatives in large-scale retrieval. |
PeerQA: A Scientific Question Answering Dataset from Peer Reviews (2025.naacl-long)
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| Challenge: | a dataset of 579 QA pairs from 208 scientific articles contains answers that reviewers raised while thoroughly examining the scientific article. |
| Approach: | They propose a dataset that contains questions that reviewers raised while thoroughly examining the scientific article. |
| Outcome: | The proposed dataset contains 579 QA pairs from 208 academic articles . the results show that decontextualization approaches improve retrieval performance . |
Generalising LLM Routing using Past Performance Retrieval: A Few-Shot Router is Sufficient (2026.eacl-srw)
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| Challenge: | Large Language Models (LLMs) use a single LLM to perform tasks. |
| Approach: | They propose a meta-evaluation framework that predicts per-model performance for new queries by retrieving similar past queries and reweighting model scores with lightweight attention. |
| Outcome: | The proposed framework matches the quality–cost trade-offs of generalisable routers across five routing benchmarks. |
Mind Your Special Tokens! On the Importance of Dedicated Sequence-End Tokens in Vision-Language Embedding Models (2026.eacl-short)
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| Challenge: | Large Vision-Language Models (LVLMs) are highly sensitive to end-of-input artifacts in fine-tuning and inference data, e.g., whether input sequences end with punctuation or newline characters. |
| Approach: | They propose to convert generative LVLMs into vision-language encoders via contrastive learning objectives and use supervised contrastive objectives to train them. |
| Outcome: | The proposed approach improves visual and text representations and improves retrieval and (semantic) similarity tasks. |
Joint Learning from Labeled and Unlabeled Data for Information Retrieval (C18-1)
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| Challenge: | Recent studies have focused on neural information retrieval (IR) models. |
| Approach: | They propose a framework which can benefit from both labeled and more abundant unlabeled data . they propose supervised retrieval over several strong baselines for IR . |
| Outcome: | The proposed framework can benefit from labeled and more abundant unlabeled data for representation learning in the context of IR. |
HotelMatch-LLM: Joint Multi-Task Training of Small and Large Language Models for Efficient Multimodal Hotel Retrieval (2025.acl-long)
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| Challenge: | a novel multimodal dense retrieval model for the travel domain addresses limitations of traditional search engines. |
| Approach: | They propose a multimodal dense retrieval model that enables natural language property search . they propose combining a small language model and a large language model for embedding hotel data . |
| Outcome: | The proposed model outperforms state-of-the-art models on four diverse test sets . it is generalizable across LLM architectures and scalability for processing large image galleries . |
BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions (D19-57)
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| Challenge: | Our best systems achieved 1st rank and scored 0.86 mAP and 0.58 macro average accuracy in Task-1 and Task-2 respectively. |
| Approach: | They propose to use attention-based supervised neural topic model and SVM for retrieval and ranking of PubMed abstracts and to use BM25 and other relevance measures for re-ranking. |
| Outcome: | The proposed system scored 0.86 mAP and 0.58 macro average accuracy in the RDoC Tasks of BioNLP-OST 2019 . |
QuackIR: Retrieval in DuckDB and Other Relational Database Management Systems (2025.emnlp-industry)
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| Challenge: | Existing vector databases for RAG are needed for large language models, but there are no alternatives. |
| Approach: | They propose to leverage existing relational databases for retrieval-augmented generation . they use duckDB, SQLite, and PostgreSQL integrations to demonstrate their effectiveness . |
| Outcome: | The proposed approach is comparable to existing IR tools. |
Reliable Evaluation Protocol for Low-Precision Retrieval (2026.acl-short)
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| Challenge: | Recent studies have shown that low-precision methods can improve performance, but they introduce high variability in the results based on tie resolution. |
| Approach: | They propose a retrieval evaluation protocol designed to reduce tie variation . high-precision scoring and tie-aware retrieval metrics are proposed to reduce this variability . |
| Outcome: | The proposed retrieval evaluation protocol reduces tie-induced instability and recovers expected scores and ranges on 12 retrieval datasets. |
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. |
Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation (2021.acl-short)
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| Challenge: | Early fusion models with cross-attention have shown better-than-human performance on some question answer benchmarks, while it is a poor fit for retrieval since it prevents pre-computation of the answer representations. |
| Approach: | They propose a supervised data mining method to train an efficient late fusion retrieval model by using cross-attention models with cross-references. |
| Outcome: | The proposed model outperforms retrieval models trained with gold annotations on Precision at N (P@N) and Mean Reciprocal Rank (MRR). |
Questions Are All You Need to Train a Dense Passage Retriever (2023.tacl-1)
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| Challenge: | Existing methods for dense retrieval require large supervised datasets with custom hard-negative mining and denoising of positive examples. |
| Approach: | They propose a new corpus-level autoencoding approach for training dense retrieval models that does not require labeled training data. |
| Outcome: | The proposed method matches or surpasses strong supervised performance levels on multiple QA benchmarks with no labeled training data or task-specific losses. |
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Question Answering Task (2026.findings-eacl)
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| Challenge: | Existing studies focus on English as the data language for RAG, resulting in limited coverage of multilingual RAG. |
| Approach: | They propose a method that translates retrieved documents into a common language before generating the response. |
| Outcome: | The proposed approach improves efficiency on knowledge-intensive tasks but introduces inconsistencies due to cross-lingual variations in the retrieved content. |
Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization (2024.findings-emnlp)
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| Challenge: | Recent work on prompt programs has focused on simple prompt programs or assumed that the structure of a prompt program is fixed. |
| Approach: | They propose a framework to perform symbolic prompt program search for compile-time optimizations of prompt programs. |
| Outcome: | The proposed framework improves performance of complex prompts on instruction tuning, pipeline tuning, prompt compression and more. |
ELIOT: Zero-Shot Video-Text Retrieval through Relevance-Boosted Captioning and Structural Information Extraction (2025.naacl-srw)
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| Challenge: | Recent advances in video-text retrieval (VTR) have relied on supervised learning and fine-tuning. |
| Approach: | They propose a zero-shot video-text retrieval framework that leverages off-the-shelf captioners, large language models, and text retrieval methods without additional training or annotated data. |
| Outcome: | The proposed framework outperforms existing methods on video-text retrieval benchmarks without data. |
Open Domain Question Answering over Tables via Dense Retrieval (2021.naacl-main)
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| Challenge: | Recent advances in open-domain QA focus on retrieving textual passages . a retriever designed to handle tabular context can improve retrieval quality . |
| Approach: | They propose a tabular-based retrieval model that improves retrieval quality over a BERT-based retriever. |
| Outcome: | The proposed retriever improves retrieval quality with mined hard negatives over a BERT-based retriever. |
DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities (2024.emnlp-main)
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| Challenge: | Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments. |
| Approach: | They propose to enhance the LSR vocabulary with Wikipedia concepts and entities, enabling the model to resolve ambiguities more effectively and stay current with evolving knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art models across three entity-rich document ranking datasets. |
Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries (2021.emnlp-main)
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| Challenge: | Existing databases contain tens of millions of molecules; PubChem alone has 110 million compounds. |
| Approach: | They propose a task to retrieve molecules using natural language descriptions as queries . they construct a paired dataset of molecules and their corresponding text descriptions . |
| Outcome: | The proposed approach improves results from 0.372 to 0.499 MRR. |
Adaptive Nearest Neighbor Machine Translation (2021.acl-short)
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| Challenge: | kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
| Approach: | They propose a method that combines a pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
| Outcome: | The proposed method outperforms the existing model on four benchmark datasets and is open-source. |
Backtracing: Retrieving the Cause of the Query (2024.findings-eacl)
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| Challenge: | a number of online content portals allow users to ask questions to supplement their understanding. |
| Approach: | They propose a task of backtracing to retrieve the text segment that most likely caused a user query. |
| Outcome: | The proposed method improves on the backtracing task in three domains . the results show that there is room for improvement and new retrieval approaches . |
MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval (2026.acl-demo)
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| Challenge: | MED-COPILOT is an interactive research prototype for evidence-aware clinical reasoning . large language models (LLMs) are prone to hallucinations and lack verifiable evidence grounding . |
| Approach: | They propose a system that integrates GraphRAG and semantic-keyword similar-patient retrieval to support transparent clinical reasoning. |
| Outcome: | The proposed system outperforms baseline and standard RAGs on clinical note completion and medical question answering. |
Reasoning over Public and Private Data in Retrieval-Based Systems (2023.tacl-1)
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| Challenge: | Existing retrieval systems assume relevant corpora are fully (e.g., publicly) accessible, but users are often unwilling to expose their private data to entities hosting public data. |
| Approach: | They propose a split iterative retrieval problem involving iterating retrieval over multiple privacy scopes and propose 'concurrentQA' benchmark to test this problem. |
| Outcome: | The proposed method improves on the existing retrieval methods but still suffers performance degradations when applied to a dataset from a public and private distribution. |
UR2N: Unified Retriever and ReraNker (2025.coling-industry)
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| Challenge: | XTR-style retrieval on top of trained Mono-T5 reranker is suboptimal for two-stage retrieval, arguing that it is sub-optimal. |
| Approach: | They propose a unified encoder-decoder architecture with a novel training regimen which enables the encoder representation to be used for retrieval and the decoder for re-ranking within a single unified model. |
| Outcome: | The proposed architecture outperforms ColBERT, XTR, and even serves as a superior reranker compared to the Mono-T5 re-ranker. |
Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension (D18-1)
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| Challenge: | Existing QA models rely on learning interaction between document and question . current models require explicit attention to the document before or as it reads it . |
| Approach: | They propose a modular question answering task that enforces complete independence of the document encoder from the question encoder. |
| Outcome: | The proposed model achieves reasonable accuracy but significantly underperforms unconstrained QA models. |
Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation (2022.coling-1)
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| Challenge: | Existing methods for generating open-domain dialogue systems underutilize training data. |
| Approach: | They propose a retrieval-generation training framework that takes advantage of heterogeneous training data by considering them as "evidence" they use BERTScore retrieval framework which gives better qualities of the training data, they show . |
| Outcome: | The proposed method performs well on zero-shot experiments and is more robust to real-world data. |
Long-Context Long-Form Question Answering for Legal Domain (2026.eacl-industry)
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| Challenge: | Legal documents have complex document layouts involving multiple nested sections and lengthy footnotes that make question answering challenging. |
| Approach: | They propose a question answering system that parses document layouts while isolating sections and footnotes and linking them appropriately. |
| Outcome: | The proposed system can parse complex document layouts while isolating sections and footnotes and linking them appropriately. |
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding (2024.eacl-long)
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| Challenge: | Recent trends in learning monolingual and multilingual sentence embeddings are based on contrastive learning (CL) among an anchor, one positive and multiple negative instances. |
| Approach: | They propose to leverage multiple positives to improve learning of multilingual sentence embeddings by using an anchor, one positive, and multiple negative instances. |
| Outcome: | The proposed approach improves retrieval, semantic similarity, and classification performance on unseen languages. |
BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain (2024.findings-emnlp)
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| Challenge: | Existing work adopts separate modules for retrieval and generation, which may be suboptimal since the retrieval task and generation task cannot benefit from each other to improve performance. |
| Approach: | They propose a backbone-shared RAG framework that uses a domain-specific corpus to continuously pre-train a model and then trains two plug-and-play Low-Rank Adaptation modules based on the shared backbone to minimize retrieval and generation losses respectively. |
| Outcome: | The proposed framework outperforms baseline models by 5% and 13% in Hit@3 upon two datasets in retrieval evaluation and by 23% in terms of BLEU-3 in generation evaluation. |
RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models (2024.emnlp-main)
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| Challenge: | Existing medical large vision language models often generate inaccurate and irrelevant answers that do not align with established medical facts. |
| Approach: | They propose a strategy for controlling factuality risk through calibrated selection of the number of retrieved contexts and a preference dataset to fine-tune the model. |
| Outcome: | The proposed model achieves an average improvement of 20.8% on three medical VQA datasets. |
ARQA: A Benchmark for Grounded Table–Text QA in Enterprise Annual Reports (2026.eacl-industry)
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| Challenge: | Existing QA benchmarks focus on retrieval or single-modality reasoning . annual reports are a company's definitive record of performance . |
| Approach: | They propose an annual report QA benchmark that compares QAs with lookups, arithmetics, and insights. |
| Outcome: | The proposed benchmarks show strong factual retrieval but persistent weaknesses in grounded arithmetic and causal reasoning. |
InstructPTS: Instruction-Tuning LLMs for Product Title Summarization (2023.emnlp-industry)
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| Challenge: | E-commerce product catalogs contain billions of items with lengthy titles . this leads to a gap between how customers refer to these unnatural titles - and how they are used . |
| Approach: | They propose a novel approach to product title summarization that uses a fine-tuned instruction strategy to train a highly accurate model. |
| Outcome: | The proposed approach can generate more accurate product title summaries with an improvement of over 14 and 8 BLEU and ROUGE points. |
RxLens: Multi-Agent LLM-powered Scan and Order for Pharmacy (2025.naacl-industry)
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| Challenge: | paper prescriptions are difficult for customers to interpret and are often unstructured, handwritten, and illegible. |
| Approach: | They propose a multi-step Large Language Model-based solution for automated pharmacy cart construction. |
| Outcome: | The proposed solution can yield up to 19% - 40% and 11% - 26% increase in Recall@3 relative to SOTA methods. |
Hashing based Efficient Inference for Image-Text Matching (2021.findings-acl)
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| Challenge: | Recent work on image-text matching has focused on exploring interactions between images and sentences to improve performance without considering inference efficiency. |
| Approach: | They propose a hashing-based efficient inference module which can be plugged into existing frameworks to speed up inference step without reducing retrieval performance. |
| Outcome: | The proposed module can be plugged into existing framework to speed up inference step without reducing retrieval performance. |
In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation (2025.findings-naacl)
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| Challenge: | Existing studies have shown that in-context examples for machine translation are beneficial for high-resource languages. |
| Approach: | They propose to use in-context examples for machine translation (MT) they argue that similarity-based selection can improve MT . |
| Outcome: | The proposed approach improves machine translation (MT) and low-resource languages. |
Diffusion-Pretrained Dense and Contextual Embeddings (2026.acl-industry)
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| Challenge: | pplx-embed uses diffusion-based pretraining to capture bidirectional context within passages. |
| Approach: | They propose a family of multilingual embedding models that leverage bidirectional attention through diffusion-based pretraining to capture bidirectional context within passages. |
| Outcome: | The proposed models achieve competitive performance on the MTEB(Multilingual, v2), MTEF(Code), BERGEN, and ToolRet retrieval benchmarks while pplx-embed-context-v1 sets new records on the ConTEB benchmark. |
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval (2024.emnlp-main)
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| Challenge: | a conversational search system requires accurate interpretation of user intent from complex multi-turn contexts. |
| Approach: | They propose a dual-learning approach that adapts LLMs for retrieval via contrastive learning while enhancing the complex session understanding through masked instruction tuning. |
| Outcome: | The proposed approach outperforms existing retrieval methods on five conversational search benchmarks. |
Adaptive Hyper-parameter Learning for Deep Semantic Retrieval (2023.emnlp-industry)
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| Challenge: | Existing methods for deep semantic retrieval are highly sensitive to hyper-parameters . a novel adaptive metric learning method is proposed to overcome this limitation . |
| Approach: | They propose a method that adaptively obtains hyper-parameters without fixed or extra-trainable hyper-parmeters . they adopt a symmetric metric learning method to mitigate model collapse issues . |
| Outcome: | The proposed method outperforms existing methods on a real-world dataset and brings economic benefits. |
Getting To Know You: User Attribute Extraction from Dialogues (2020.lrec-1)
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| Challenge: | a new method to extract user attributes from dialogues is needed to improve user understanding. |
| Approach: | They propose to leverage dialogues with conversational agents to automatically extract user attributes from dialogues. |
| Outcome: | The proposed model surpasses retrieval and generation baselines on human evaluation. |
Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation (2024.emnlp-main)
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| Challenge: | Existing methods for legal case retrieval often overlook the incorporation of legal expert knowledge, leading to unsatisfactory retrieval performance. |
| Approach: | They propose a legal knowledge-guided case reformulation approach based on large language models for effective and interpretable legal case retrieval. |
| Outcome: | The proposed model performs better on complex legal case queries than existing methods. |
EqualizeIR: Mitigating Linguistic Biases in Retrieval Models (2025.naacl-short)
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| Challenge: | Existing information retrieval models show significant linguistic biases based on the linguistic complexity of queries. |
| Approach: | They propose a framework to mitigate linguistic biases in IR models by using a linguistically biased weak learner to capture biased queries and then train a robust model by regularizing and refining its predictions. |
| Outcome: | The proposed framework reduces performance disparities across simple and complex queries while improving overall retrieval performance. |
Condenser: a Pre-training Architecture for Dense Retrieval (2021.emnlp-main)
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| Challenge: | Prior work fine-tunes deep LMs to encode text sequences into single dense vector representations, but dense encoders require a lot of data and sophisticated techniques to train and suffer in low data situations. |
| Approach: | They propose to pre-train Transformer language models (LMs) with a novel Transformer architecture, Condenser, where LM prediction CONditions on DENSE Representation. |
| Outcome: | The proposed model improves on various text retrieval and similarity tasks by large margins over standard LMs. |
OMG-QA: Building Open-Domain Multi-Modal Generative Question Answering Systems (2024.emnlp-industry)
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| Challenge: | Existing approaches to QA require multiple modalities and a broad pool of information sources to generate coherent answers. |
| Approach: | They propose a new resource to evaluate the effectiveness of question answering systems that perform retrieval augmented generation in scenarios that demand reasoning on multi-modal, multi-document contexts. |
| Outcome: | The proposed method evaluates question answering systems that perform retrieval augmented generation (RAG) in open-domain questions . it requires systems to navigate diverse modalities and a broad pool of information sources, making it uniquely challenging. |
Data-Centric Perspectives on Agentic Retrieval-Augmented Generation: A Survey (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel at natural language understanding and generation, yet rely on static pre-training data. |
| Approach: | They propose to augment Large Language Models with external retrieval to ground model outputs . traditional RAG is constrained by a fixed retrieve-then-generate routine . authors aim to guide creation of high-quality datasets for next generation of adaptive LLM agents . |
| Outcome: | The proposed model can decompose tasks, issue exploratory queries, and refine evidence through iterative retrieval. |
Citation-Enhanced Generation for LLM-based Chatbots (2024.acl-long)
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| Challenge: | Existing efforts to alleviate hallucination in chatbots require additional training and data annotation. |
| Approach: | They propose a Citation-Enhanced Generation approach that uses retrieval argumentation to generate citations and a natural language inference-based citation generation module to generate content. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three benchmarks. |
DRUM: Learning Demonstration Retriever for Large MUlti-modal Models (2025.acl-srw)
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| Challenge: | Recent studies have shown that large language models (LLMs) have impressive capabilities in dealing with new tasks with the help of in-context learning (ICL). |
| Approach: | They propose to concate the image and text embeddings to enhance the retrieval performance of a visual-language task and to calculate a list-wise ranking loss for training the embeddable model. |
| Outcome: | The proposed framework fine-tunes the CLIP embedding model to better meet the needs of the large vision-language models. |
COFAR: Commonsense and Factual Reasoning in Image Search (2022.aacl-main)
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Prajwal Gatti, Abhirama Subramanyam Penamakuri, Revant Teotia, Anand Mishra, Shubhashis Sengupta, Roshni Ramnani
| Challenge: | Existing approaches to retrieve relevant images for natural language searches are limited by visual recognition and lack of commonsense reasoning. |
| Approach: | They propose a framework that leverages visual content and natural language queries to enable commonsense reasoning and factual reasoning in the image search. |
| Outcome: | The proposed framework enables commonsense and factual reasoning in image search on a COFAR dataset. |
REST: Retrieval-Based Speculative Decoding (2024.naacl-long)
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| Challenge: | Retrieval-based speculative decoding (REST) is a new language model generation algorithm . it uses existing knowledge to generate draft tokens, allowing for seamless integration and acceleration of any language model. |
| Approach: | They propose a new algorithm that uses a draft language model to generate tokens from existing knowledge. |
| Outcome: | The proposed method achieves a speedup of 1.62 to 2.36 on code or text generation. |
UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generation (2024.emnlp-main)
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| Challenge: | e-commerce tasks such as multimodal retrieval and multimodal generation are largely ignored due to the diversity of the multimodal fashion domain. |
| Approach: | They propose a framework that integrates image generation with retrieval and text generation tasks. |
| Outcome: | The proposed framework outperforms state-of-the-art models across fashion tasks. |
Chain-of-Skills: A Configurable Model for Open-Domain Question Answering (2023.acl-long)
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| Challenge: | Using customized retrieval models, model transferability and scalability are limited. |
| Approach: | They propose a modular retrieval model where individual modules correspond to key skills that can be reused across datasets. |
| Outcome: | The proposed model outperforms self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA. |
Reinforcement Learning for Adversarial Query Generation to Enhance Relevance in Cold-Start Product Search (2025.acl-industry)
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| Challenge: | Existing methods do not incorporate feedback from the query relevance model, limiting their ability to generate queries that enhance product retrieval. |
| Approach: | They propose an adversarial reinforcement learning framework that exposes weaknesses in query classification models by creating synthetic queries that augment the classifier's training set. |
| Outcome: | The proposed framework improves query generation performance on public datasets and on proprietary datasets. |
Logical Consistency is Vital: Neural-Symbolic Information Retrieval for Negative-Constraint Queries (2025.findings-acl)
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Ganlin Xu, Zhoujia Zhang, Wangyi Mei, Jiaqing Liang, Weijia Lu, Xiaodong Zhang, Zhifei Yang, Xiaofeng Ma, Yanghua Xiao, Deqing Yang
| Challenge: | Current dense retrieval methods compute similarities between dense vectors but overlook the real query intents. |
| Approach: | They propose a neuro-symbolic information retrieval method that leverages first-order logic to optimize the embeddings of naive natural language by considering the logical consistency between queries and documents. |
| Outcome: | The proposed method outperforms existing methods on negative-constraint queries under zero-shot and low-resource retrieval tasks. |
Virtual Knowledge Graph Construction for Zero-Shot Domain-Specific Document Retrieval (2022.coling-1)
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| Challenge: | Domain-specific documents cover terminologies and specialized knowledge. |
| Approach: | They propose a domain-specific document retrieval method that embeds a document into a graph of entities and their relations into . they compare the unsupervised method with previous approaches and use it to compute relevance between queries and documents. |
| Outcome: | The proposed method outperforms baselines and fully-supervised bi-encoders in a zero-shot setting and outperformed bi-supervised approaches. |
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the CHATGPT Era and Beyond (2024.eacl-long)
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Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Viktor Schlegel, Stefan Winkler, See-Kiong Ng, Soujanya Poria
| Challenge: | Sentence representations are a critical component in NLP applications such as retrieval, question answering, and text classification. |
| Approach: | They present a systematic review of the literature on sentence representations focusing mostly on deep learning models. |
| Outcome: | The proposed methods highlight the key contributions and challenges in this area and suggest potential avenues for improving the quality and efficiency of sentence representations. |
Can’t Hide Behind the API: Stealing Black-Box Commercial Embedding Models (2025.findings-naacl)
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| Challenge: | a new study examines the retrieval effectiveness of commercial embedding models . robert mcgahey: can commercial embeds be "stolen" using distillation techniques? he says stealing models can offer benefits to different actors, including reduced costs and security . |
| Approach: | They propose to "steal" embedding models by training thief models on text–embedding pairs . they replicate retrieval effectiveness of commercial embeddable models with a cost of under $300 . |
| Outcome: | The proposed methods replicate retrieval effectiveness of commercial embedding models with under $300 . authors suggest measures to mitigate risk of model theft. |
EcoDoc: A Cost-Efficient Multimodal Document Processing System for Enterprises Using LLMs (2025.acl-industry)
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| Challenge: | Recent advances in Retrieval-Augmented Generation (RAG) frameworks and Vision-Language Models (VLMs) have improved retrieval performance on multimodal documents by processing pages as images. |
| Approach: | They propose a cost-effective multimodal document processing system that dynamically selects the processing modalities for each page as an image or text based on page characteristics and query intent. |
| Outcome: | The proposed system reduces average query processing latency by 2.29 and cost by up to 10 . it reduces cost and latency while maintaining high performance on large scale deployments . |
On the Generalization Ability of Retrieval-Enhanced Transformers (2023.findings-eacl)
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| Challenge: | Recent work on retrieval-augmented language models has shown impressive results . performance gains from retrieval to a large extent originate from overlapping tokens between the database and test data, suggesting less of non-trivial generalization than previously assumed. |
| Approach: | They propose to off-load memory from trainable weights to a retrieval database and compare it to larger models with a larger model. |
| Outcome: | The proposed model outperforms GPT-3 and Jurassic-1 on the Pile at 4% of the model parameters. |
Culinary Crossroads: A RAG Framework for Enhancing Diversity in Cross-Cultural Recipe Adaptation (2026.acl-long)
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| Challenge: | Retrieval-augmented generation (RAG) is a promising approach for cross-cultural recipe adaptation, but it fails to generate diverse results even when provided with varied contextual inputs. |
| Approach: | They propose a plug-and-play RAG framework that enhances diversity in both retrieval and context organization to generate diverse outputs to accommodate multiple user preferences. |
| Outcome: | The proposed framework achieves Pareto efficiency in terms of diversity and quality of recipe adaptation compared to closed-book LLMs. |
Is Semantic Chunking Worth the Computational Cost? (2025.findings-naacl)
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| Challenge: | Recent advances in Retrieval-Augmented Generation (RAG) systems have popularized semantic chunking. |
| Approach: | They evaluate the effectiveness of semantic chunking using three common retrieval tasks . they find that the computational costs associated with semantic chunks are not justified by consistent performance gains. |
| Outcome: | The proposed semantic chunking approach is not able to deliver consistent performance gains in three retrieval-related tasks. |
Query-as-context Pre-training for Dense Passage Retrieval (2023.emnlp-main)
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| Challenge: | Existing methods to improve passage retrieval performance by using context-supervised pre-training are weakly correlated. |
| Approach: | They propose to use query-as-context pre-training to train passage-query pairs . they evaluate the pre-trained models on large-scale passage retrieval benchmarks . |
| Outcome: | The proposed technique improves performance on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks. |
RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark (2026.findings-acl)
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| Challenge: | Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both. |
| Approach: | They propose a framework that transforms complex queries into fact-grounded multi-turn dialogues through multi-level validation. |
| Outcome: | The proposed framework outperforms existing systems in a number of domains and can be used to improve multi-turn conversation retrieval. |
Analysis of Automated Document Relevance Annotation for Information Retrieval in Oil and Gas Industry (2025.emnlp-industry)
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João Vitor Mariano Correia, Murilo Missano Bell, João Vitor Robiatti Amorim, Jonas Queiroz, Daniel Pedronette, Ivan Rizzo Guilherme, Felipe Lima de Oliveira
| Challenge: | Lack of high-quality test collections challenges Information Retrieval (IR) in specialized domains. |
| Approach: | They compare supervised classifiers against zero-shot Large Language Models for automated relevance annotation in the oil and gas industry using human expert judgments as a benchmark. |
| Outcome: | The proposed classifier outperforms LLMs in the oil and gas industry using human expert judgments. |
Measuring and Mitigating Racial Bias in Embedding Models: A Comparative Study for Law Enforcement Retrieval (2026.acl-industry)
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| Challenge: | Embedding models are often used for semantic retrieval in high-stakes domains such as law enforcement . racial descriptors affect similarity scores and retrieval rankings for semantically identical crime incidents . |
| Approach: | They propose to use racial descriptors to measure r&d bias in embedding models . they compute similarity scores between crime incidents and simple law enforcement queries . |
| Outcome: | The proposed methods show that racial descriptors affect similarity scores and retrieval rankings for semantically identical crime incidents. |
RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models (2023.acl-long)
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| Challenge: | Existing methods for retrieval-oriented language models focus on contextualized embedding of the [CLS] token, but recent study shows that ordinary tokens besides [CLL] may provide extra information, which help to produce a better representation effect. |
| Approach: | They propose a method where all contextualized embeddings of pre-trained model can be jointly pre-trained for retrieval tasks. |
| Outcome: | The proposed method improves the quality of representation where all contextualized embeddings of the pre-trained model can be leveraged. |
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment (2025.acl-long)
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| Challenge: | Existing personalized product search methods assume that users’ query fully captures their real motivation, but in practice, user's queries do not always articulate the requirements. |
| Approach: | They propose a Motivation-Aware Personalized Search method that embeds queries and consultations into a unified semantic space via LLMs and utilizes a Mixture of Attention Experts (MoAE) to prioritize critical semantics. |
| Outcome: | Extensive experiments on real and synthetic data show that the proposed method outperforms existing methods in retrieval and ranking tasks. |
ExpertGenQA: Open-ended QA generation in Specialized Domains (2025.findings-emnlp)
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| Challenge: | Existing methods for generating high-quality question–answer (QA) pairs yield generic or shallow questions that fail to reflect the depth and structure of expert-written examples. |
| Approach: | They propose a question-answer generation protocol that combines few-shot prompting with dual categorization by topic and question style to produce more diverse and cognitively meaningful QA pairs. |
| Outcome: | The proposed protocol achieves twice the efficiency of standard few-shot methods while maintaining 94.4% topic coverage. |
pEBR: A Probabilistic Approach to Embedding Based Retrieval (2025.emnlp-industry)
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| Challenge: | Existing embedding-based retrieval systems rely on heuristic and suboptimal cutoffs for item retrieval. |
| Approach: | They propose a probabilistic Embedding-Based Retrieval framework that learns a shared semantic representation space for both queries and items. |
| Outcome: | The proposed framework improves retrieval precision and recall, and ablation studies show it captures the differences between head-to-tail queries. |
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. |
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG (2025.findings-naacl)
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| Challenge: | Retrieval-Augmented Generation (RAG) is widely adopted in Large Language Models, but is flat and has limitations such as a significant burden on one retriever and constant granularity limits the ceiling of retrieval performance. |
| Approach: | They propose a progressive retrieval paradigm with coarse-to-fine granularity for RAG, termed FunnelRAG, so as to balance effectiveness and efficiency. |
| Outcome: | The proposed paradigm achieves comparable retrieval performance while the time overhead is reduced by nearly 40%. |
GeAR: Generation Augmented Retrieval (2025.findings-acl)
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Haoyu Liu, Shaohan Huang, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Furu Wei, Qi Zhang
| Challenge: | Document retrieval techniques are used to compute semantic similarity between a query and documents, but the scalar similarity fails to reflect enough information, hindering the interpretation of retrieval results. |
| Approach: | They propose a method which improves the global document-query similarity through contrastive learning and integrates well-designed fusion and decoding modules. |
| Outcome: | The proposed method improves the global document-query similarity through contrastive learning and integrates well-designed fusion and decoding modules. |
T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning (2025.emnlp-industry)
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| Challenge: | Generic embedding models struggle to represent telecom-specific semantics . specialized terminology and ambiguous terms often limit their utility in retrieval and downstream tasks. |
| Approach: | They propose a domain-adapted embedding model fine-tuned from a gte-Qwen2-1.5B-instruct backbone. |
| Outcome: | The proposed model outperforms MPNet, BGE, Jina and E5 on a custom benchmark . it is open source and has a triplet loss objective . |
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval (2022.findings-acl)
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| Challenge: | Existing methods for entity retrieval use mention boundaries but unstructured text . state-of-the-art methods struggle to retrieve rare entities for ambiguous mentions . |
| Approach: | They propose a method to jointly train bi-encoders on knowledge graph types and unstructured text for entity retrieval for open-domain tasks. |
| Outcome: | The proposed method improves retrieval of rare entities on Ambiguous Entity Retrieval sets while maintaining strong overall retrieval performance on open-domain tasks. |
UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities (2026.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. |
| Approach: | They propose a framework to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities. |
| Outcome: | The proposed framework shows superiority over existing methods on 10 benchmarks of multiple modalities. |
A Compliance Checking Framework Based on Retrieval Augmented Generation (2025.coling-main)
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| Challenge: | Existing text-based compliance checking methods are limited by their flexibility and lack structure. |
| Approach: | They propose a text-based compliance checking framework based on Retrieval-Augmented Generation that integrates a static layer for storing factual knowledge, a dynamic layer for retrieval and reasoning, and an eventic graph to structurally describe regulatory information. |
| Outcome: | The proposed framework consistently achieves state-of-the-art results across various scenarios surpassing baselines. |
FinMTEB: Finance Massive Text Embedding Benchmark (2025.emnlp-main)
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| Challenge: | Existing text embedding benchmarks for financial domains are inadequately addressing the nuanced requirements of specialized domains like finance. |
| Approach: | They propose a finance-adapted embedding model that outperforms general-purpose models . they also introduce a new model, Fin-E5, which is also open-sourced . |
| Outcome: | The proposed framework outperforms general-purpose models on financial embedding tasks. |
Eval-RAR: Evaluation-Driven Retrieval-Augmented Reasoning via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing methods for retrieval-augmented generation fail to provide explicit supervision for internal reasoning process. |
| Approach: | They propose an Evaluation-driven Retrieval-Augmented Reasoning framework that uses reinforcement learning and a fine-grained evaluation reward to optimize the process. |
| Outcome: | Eval-RAR outperforms existing methods on QA benchmarks on seven single-hop and multi-hop tasks. |
Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models (2025.findings-emnlp)
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| Challenge: | Extract-Refine-Retrieve-Read is a query optimization framework for large language models . it is designed to bridge the pre-retrieval information gap in Retriev-Augmented Generation systems . |
| Approach: | They propose a framework to extract parametric knowledge from Large Language Models and refine them using a specialized query optimizer. |
| Outcome: | The extract-refine-retrieve-read framework outperforms baselines on QA datasets . it is designed to meet the knowledge requirements of large language models (LLMs) |
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework (D19-1)
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| Challenge: | generative models for end-to-end sequence generation have been shown promising for this task . however, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator is still challenging. |
| Approach: | They propose a framework where skeleton extraction is made by an interpretable matching model and a retrieval-guided response generator is followed by a separate generator. |
| Outcome: | The proposed framework outperforms baseline models in a variety of experiments. |
Retrieval Augmented Instruction Tuning for Open NER with Large Language Models (2025.coling-main)
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| Challenge: | Existing studies have focused on integrating large language models (LLMs) with information extraction (IE) however, the best approach to incorporate information with LLMs for IE remains an open question. |
| Approach: | They propose to use a Chinese IT dataset to perform RA-IT for IE . they use semantically similar examples from the training dataset as the context . |
| Outcome: | The proposed approach is evaluated in English and Chinese scenarios. |
Complex Claim Verification with Evidence Retrieved in the Wild (2024.naacl-long)
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| Challenge: | Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: no access to evidence, access to curated evidence, or access to published evidence after a claim was made. |
| Approach: | They propose a pipeline to check claims using raw evidence from the web . they restrict their retriever to only search documents available prior to the claim's making . |
| Outcome: | The proposed method is based on a political claim dataset and shows that the evidence summary produced by the system is reliable and relevant to answering key questions. |
KoBLEX: Open Legal Question Answering with Multi-hop Reasoning (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performances in general domains and are now extending into the expert domain of law. |
| Approach: | They propose a Korean Benchmark for Legal EXplainable QA (KoBLEX) that evaluates provision-grounded, multi-hop legal reasoning. |
| Outcome: | The proposed method outperforms baselines and shows a high correlation with human judgments. |
Accelerating Learned Sparse Indexes Via Term Impact Decomposition (2022.findings-emnlp)
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| Challenge: | Novel inverted index-based learned sparse ranking models provide more effective, but less efficient, retrieval performance compared to traditional ranking models. |
| Approach: | They propose a technique that allows for automatic pruning of ranking models by storing metadata about index term importance scores. |
| Outcome: | The proposed technique accelerates top-k retrieval by 9.6X without loss in effectiveness. |
Huatuo-26M, a Large-scale Chinese Medical QA Dataset (2025.findings-naacl)
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Xidong Wang, Jianquan Li, Shunian Chen, Yuxuan Zhu, Xiangbo Wu, Zhiyi Zhang, Xiaolong Xu, Junying Chen, Jie Fu, Xiang Wan, Anningzhe Gao, Benyou Wang
| Challenge: | Large Language Models are a powerful tool for medical research, but the data is a bottleneck. |
| Approach: | They propose to use the largest ever medical Question Answering dataset with 26 Million QA pairs as a fine-tuning data for training large language models. |
| Outcome: | The proposed dataset demonstrates that it can be used to train large language models and improves zero-shot performance on other datasets. |
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering (2025.findings-emnlp)
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| Challenge: | Existing retrieval approaches often overlook patient-specific factual knowledge embedded in EHRs . existing retrieval frameworks often overlook this factual information, limiting its effectiveness in clinical decision-making. |
| Approach: | They propose a recurrence generation-augmented retrieval framework that synergizes factual and conceptual knowledge from dual sources. |
| Outcome: | The proposed framework improves on factual-aware medical QA benchmarks. |
CSMCIR: CoT-Enhanced Symmetric Alignment with Memory Bank for Composed Image Retrieval (2026.findings-acl)
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Zhipeng Qian, Zihan Liang, Yufei Ma, Ben Chen, Huangyu Dai, Yiwei Ma, Jiayi Ji, Chenyi Lei, Han Li, Xiaoshuai Sun
| Challenge: | Existing approaches to search for images using single-modality are limited by representation space fragmentation. |
| Approach: | They propose a unified representation framework that achieves efficient query-target alignment . they introduce a multi-level Chain-of-Thought prompting strategy that guides MLMs to generate discriminative, semantically compatible captions for target images . |
| Outcome: | The proposed framework achieves efficient query-target alignment through synergistic components. |
Towards Improved Multi-Source Attribution for Long-Form Answer Generation (2024.naacl-long)
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| Challenge: | Current LLMs struggle with attribution for long-form answers which require reasoning over multiple evidence sources. |
| Approach: | They propose to improve attribution capability of large language models for long-form answer generation to multiple sources with multiple citations per sentence. |
| Outcome: | The proposed model improves on a wide range of attribution benchmark datasets on PolitiICite, a multi-source attribution dataset based on PolitIcite articles . |
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025.emnlp-main)
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Chang Su, Dengliang Shi, Siyuan Huang, Jintao Du, Changhua Meng, Yu Cheng, Weiqiang Wang, Zhouhan Lin
| Challenge: | Existing text embedding approaches often leverage the embeddment of the final token, typically a reserved special token such as ‘[EOS]‘. |
| Approach: | They propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding. |
| Outcome: | The proposed training stage improves performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales. |
MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity (2025.coling-main)
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| Challenge: | Existing RAG frameworks either indiscriminately perform retrieval or rely on rigid single-label classifiers to select retrieval methods. |
| Approach: | They propose a framework that dynamically selects the most suitable retrieval strategy based on query complexity. |
| Outcome: | The proposed framework achieves state-of-the-art results on multiple single-hop and multi-hop datasets while reducing retrieval costs. |
When Do Decompositions Help for Machine Reading? (2023.emnlp-main)
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| Challenge: | Existing work on decompositions of complex questions has focused on multi-step reasoning . but, in machine reading, it is unclear when decomposing is helpful . |
| Approach: | They conduct experiments on decompositions in machine reading to unify recent work . they find that decomposing complex questions can be helpful in zero or limited-data settings . |
| Outcome: | The proposed model can learn decompositions implicitly even with limited data, the study shows . the results are consistent with previous work on decomposing complex questions . |
Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5 (2025.naacl-long)
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| Challenge: | Existing benchmarks for evaluating retrieval models in Hindi are lacking . despite efforts to build multilingual retrieval systems, this is still a work in progress . |
| Approach: | They evaluate Hindi retrieval models on the Hindi-BEIR benchmark and introduce a multilingual model that leverages a zero-shot approach to support Hindi without the need for Hindi training data. |
| Outcome: | The proposed model leverages a zero-shot approach to support Hindi without the need for Hindi training data. |
ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation systems face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. |
| Approach: | They propose an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle. |
| Outcome: | The proposed framework achieves dual improvements in retrieval precision and generation quality without additional training or API resources while using only 40% of the tokens compared to traditional approaches. |
Relation Extraction with Type-aware Map Memories of Word Dependencies (2021.findings-acl)
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| Challenge: | Existing studies focus on the dependency connections between words with limited attention paid to exploiting dependency types. |
| Approach: | They propose a neural approach for relation extraction with type-aware map memories . they map all associated words along with dependencies among them to memory slots . |
| Outcome: | The proposed approach achieves state-of-the-art on two English benchmark datasets. |
Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions (2025.emnlp-main)
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| Challenge: | Existing approaches for optimizing human annotation efforts are limited . et al., 2015) suggest that densely annotated image captions improve vision-language alignment . |
| Approach: | They propose an AI-in-the-loop methodology to maximize the number of annotated samples and improve their comprehensiveness under fixed budget constraints. |
| Outcome: | The proposed method improves annotation speed and retrieval performance over the parallel method. |
Entity Pair-guided Relation Summarization and Retrieval in LLMs for Document-level Relation Extraction (2025.findings-naacl)
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| Challenge: | Document-level relation extraction (DocRE) aims to extract relations between entities in a document. |
| Approach: | They propose an entity pair-guided relation summarization and retrieval model for DocRE . the model uses entity pairs to guide relation summaries and retrievals . |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) performance on three datasets. |
MATCHED: Multimodal Authorship-Attribution To Combat Human Trafficking in Escort-Advertisement Data (2025.findings-acl)
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| Challenge: | Existing methods for human trafficking detection ignore the multimodal nature of online ads . sex trafficking is a pervasive crime exploiting individuals of all ages and genders . |
| Approach: | They propose to use multimodal authorship attributes to identify suspicious ads that combine text and images to improve vendor identification and verification tasks. |
| Outcome: | The proposed model outperforms existing methods for vendor identification and verification tasks using text-only, vision-only and multimodal training objectives. |
Intuitive or Dependent? Investigating LLMs’ Behavior Style to Conflicting Prompts (2024.acl-long)
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| Challenge: | Extensive experiments with seven Large Language Models reveal their varying behaviors. |
| Approach: | They investigate the behaviors of Large Language Models when faced with conflicting prompts versus their internal memory. |
| Outcome: | Extensive experiments with seven LLMs reveal their varying behaviors. |
R3Mem: Bridging Memory Retention and Retrieval via Reversible Compression (2025.findings-acl)
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| Challenge: | Existing memory solutions that store information via parameters struggle with reliable retrieval. |
| Approach: | They propose a memory network that optimizes both information Retention and Retrieval through Reversible context compression. |
| Outcome: | The proposed memory network outperforms conventional memory modules in long-horizon interaction tasks like conversational agents and achieves state-of-the-art performance in language modeling and retrieval-augmented generation tasks. |
DAPR: A Benchmark on Document-Aware Passage Retrieval (2024.acl-long)
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| Challenge: | Recent approaches to retrieve short passages are limited due to expensive operations such as selfattention. |
| Approach: | They propose to use a task to find passages from a large corpus of documents . they extend the SoTA passage retrievers with document context to achieve this task . |
| Outcome: | The proposed task is called Document-Aware Passage Retrieval (DAPR) it performs best on easy and hard queries, but fails on hard queries requiring document-context understanding . |
Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detection (2026.eacl-long)
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Ivan Vykopal, Antonia Karamolegkou, Jaroslav Kopčan, Qiwei Peng, Tomáš Javůrek, Michal Gregor, Marian Simko
| Challenge: | Recent advances in multilingual Large Language Models have enabled powerful capabilities for cross-lingual fact-checking. |
| Approach: | They evaluate six open-source multilingual LLMs across 20 languages using a fully multilingual prompting strategy. |
| Outcome: | The proposed model performs better on high-resource languages than on low-resourced ones. |
Revisiting Query Variation Robustness of Transformer Models (2024.findings-emnlp)
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| Challenge: | Despite their proficiency with natural language, transformer-based large language models are not robust to query variations such as typos and paraphrases. |
| Approach: | They extend their findings to include more recent large language models . they find that instruct-LLMs are more robust to query variations . |
| Outcome: | The proposed model can be prompted for robustness by a set of instruction-tuned LLMs. |
Interpreting User Requests in the Context of Natural Language Standing Instructions (2024.findings-naacl)
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| Challenge: | Existing approaches to LLM-based dialogue modeling provide additional context for users to make requests. |
| Approach: | They propose an approach to LLM-based dialogue modeling where persistent user constraints and preferences are provided as additional context for such interfaces. |
| Outcome: | The proposed model achieves a maximum of 46% exact match on the prediction of 2.4K English dialogues with a language-to-program dataset. |
Harnessing Abstractive Summarization for Fact-Checked Claim Detection (2022.coling-1)
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| Challenge: | Social media platforms are becoming battlegrounds for anti-social elements . fact-checking organizations cannot cope with the rapid dissemination of misinformation . a new workflow for fact- checking can be implemented to reduce human time for tasks with high cognition . |
| Approach: | They propose a workflow for detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries. |
| Outcome: | The proposed workflow achieves Recall@5 and MRR of 35% and 0.3, respectively. |
Multi-Modal Retrieval For Large Language Model Based Speech Recognition (2024.findings-acl)
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Aditya Gourav, Jari Kolehmainen, Prashanth Shivakumar, Yile Gu, Grant Strimel, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko
| Challenge: | kNN-LM and cross-attention techniques are used to extend text based retrieval to other modalities . wide adoption of large language models has driven new application areas leveraging this technology . |
| Approach: | They propose to use kNN-LM and cross-attention techniques to extend text retrieval methods to other modalities. |
| Outcome: | The proposed methods outperform text-based retrieval and improve word error rate on a speech recognition dataset. |
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases (C18-1)
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| Challenge: | Simple question answering over knowledge bases is one of the most important natural language processing tasks. |
| Approach: | They propose to conduct pattern extraction and entity linking first and put forward pattern revising procedure to mitigate the error propagation problem. |
| Outcome: | The proposed method outperforms the current state-of-the-art in this task by an absolute large margin. |
Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate (2025.findings-acl)
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| Challenge: | Existing rule retrieval methods suffer from low accuracy due to semantic gap between instantiated facts and abstract representations of rules. |
| Approach: | They propose a method that induces inferential rules that might offer benefits for reasoning by abstracting the underlying knowledge and logical structure in queries. |
| Outcome: | The proposed method improves retrieval effectiveness and accuracy across settings. |
Cross-Lingual Phrase Retrieval (2022.acl-long)
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| Challenge: | Existing approaches to cross-lingual phrase retrieval learn word or sentence representations in word or sentences. |
| Approach: | They propose a cross-lingual phrase retrieval model that extracts phrase representations from unlabeled example sentences. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a large-scale cross-lingual phrase retrieval dataset, showing it can perform in an unseen language pair during training. |
Unified Language Representation for Question Answering over Text, Tables, and Images (2023.findings-acl)
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| Challenge: | Existing approaches to answer complex questions are limited to text or structured data. |
| Approach: | They propose a paradigm that transforms images and tables into unified language representations to simplify QA problems. |
| Outcome: | The proposed framework outperforms existing methods on two datasets and the WebQA leaderboard. |
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction (N19-1)
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| Challenge: | Existing methods on keyphrase generation are purely extractive or generative . however, extractive methods cannot predict absent keyphrases which are not in the document. |
| Approach: | They propose a multi-task learning framework that jointly learns an extractive model and a generative model. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on five keyphrase generation tasks. |
Benchmarking a transformer-FREE model for ad-hoc retrieval (2021.eacl-main)
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| Challenge: | a recent study compares transformer-based models with a greener and more sustainable alternative. |
| Approach: | They compare transformer-based models with a "greener and more sustainable" alternative . they show that transformer-like models can be used in real-world retrieval applications . |
| Outcome: | The lighter model achieves a speedup of 20 times in training and 7 to 47 times in inference while maintaining a comparable retrieval performance. |
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach (2022.emnlp-main)
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| Challenge: | Existing methods for retrieving encyclopedic knowledge lack a large corpus and effective commonsense retriever. |
| Approach: | They propose a framework for retrieval-augmented commonsense reasoning with a large commonsensense corpus and a commonseense retriever. |
| Outcome: | The proposed framework outperforms existing methods on commonsense reasoning tasks. |
Investigating Language Preference of Multilingual RAG Systems (2025.findings-acl)
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| Challenge: | Empirical results show that DKM-RAG mitigates language preference in generation and enhances performance across diverse linguistic settings. |
| Approach: | They propose a framework that integrates translated passages with internal knowledge to overcome these issues. |
| Outcome: | The proposed framework mitigates language preference in generation and enhances performance across diverse linguistic settings. |
Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models (2024.naacl-long)
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| Challenge: | Large language models (LMs) excel in retrieving popular facts, but encounter difficulty with infrequent entity-relation pairs compared to retrievers. |
| Approach: | They propose to use a WiTQA dataset to explore the effects of combinations of entities and relations on LMs. |
| Outcome: | The proposed model can retain popular relations of less common entities while retaining the same popular relations. |
Beyond Sequences: Two-dimensional Representation and Dependency Encoding for Code Generation (2025.acl-long)
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| Challenge: | Existing code generation approaches represent code as a linear sequence of tokens, but positional encodings compromise generalization . explicit positional encoders sacrifice permutation invariance, imposes a strict order on the input sequence . |
| Approach: | They propose to represent code snippets as two-dimensional entities with explicit encodings . they propose to use dictionary learning to perform semantic matching between code lines . |
| Outcome: | The proposed model captures the hierarchical and spatial structure of code, especially the dependencies between code lines. |
SDA: Semantic Discrepancy Alignment for Text-conditioned Image Retrieval (2024.findings-acl)
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| Challenge: | Existing methods for textconditioned image retrieval are limited due to the scale of training and the complexity of attributes in modification texts. |
| Approach: | They propose a general boosting framework to address these issues by employing semantic discrepancy alignment by formulating distance consistency and neighbor consistency between the image and text domains. |
| Outcome: | The proposed framework improves retrieval performance on three prominent datasets with state-of-the-art results. |
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering (2025.acl-long)
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Ruisheng Cao, Hanchong Zhang, Tiancheng Huang, Zhangyi Kang, Yuxin Zhang, Liangtai Sun, Hanqi Li, Yuxun Miao, Shuai Fan, Lu Chen, Kai Yu
| Challenge: | Existing approaches to retrieval augmented generation neglect PDF structure and layout . individual PDFs often exceed prompt limits and user queries may span multiple documents. |
| Approach: | They propose a hybrid neural symbolic retrieval framework which combines both paradigms in an interactive process. |
| Outcome: | The proposed framework organizes semi-structured PDF content into relational database and vectorstore . it defeats both RAG and structured baselines on three PDF-based QA datasets . |
Iterative Self-Correction for Text-Driven Person Re-Identification with Large Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing methods for Person Re-Identification (ReID) adopt a static "one-pass" paradigm, converting images to text once for retrieval. |
| Approach: | They propose a framework that reformulates ReID as an iterative "Think-and-Refine" process. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in complex occlusion scenarios. |
KAPA: A Deliberative Agent Framework with Tree-Structured Knowledge Base for Multi-Domain User Intent Understanding (2025.findings-acl)
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Jiakai Tang, Shiqi Shen, ZhipengWang ZhipengWang, Gong Zhi, Xueyang Feng, Zexu Sun, Haoran Tan, Xu Chen
| Challenge: | Existing studies on the use of LLMs for estimating user intents are either too far from real human thought processes or require labeled samples. |
| Approach: | They propose a deliberative agent framework that leverages human thought process to build high-level domain knowledge and a tree-structured knowledge base to store refined experience and data. |
| Outcome: | The proposed framework is able to build high-level domain knowledge and efficiently store it across multiple steps. |
TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Existing approaches to retrieval-augmented generation rely on fragment-level retrieval . GraphRAG suffers from inefficiencies in information extraction and costly resource consumption . |
| Approach: | They propose a tag-guided hierarchical knowledge graph RAG framework for efficient global reasoning and scalable graph maintenance. |
| Outcome: | GraphRAG achieves an average win rate of 78.36% on a dataset spanning agriculture, computer science, law, and cross-domain settings compared with baselines . |
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. |
Evaluating Multi-Hop Reasoning in Large Language Models: A Chemistry-Centric Benchmark (2026.findings-eacl)
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Mohammad Khodadad, Ali Shiraee Kasmaee, Mahdi Astaraki, Nicholas Sherck, Hamidreza Mahyar, Soheila Samiee
| Challenge: | a new pipeline for compositional multi-hop reasoning in large language models is being developed . a recent study shows that even state-of-the-art models struggle with compositional reasoning . |
| Approach: | They propose a pipeline that builds benchmarks from proprietary or public data . they use generative reasoning models, chemical named-entity recognition, and external knowledge bases to build knowledge graphs. |
| Outcome: | The proposed pipeline compares state-of-the-art models with and without retrieval augmentation . the pipeline is generalizable with fine-tuning, enabling creation of challenging benchmarks . |
An Initial Test Collection for Ranked Retrieval of SMS Conversations (L18-1)
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| Challenge: | a test collection for retrieving SMS content is described . the collection contains 31 topics, which are considered too few for reliable statistical significance tests. |
| Approach: | They describe a test collection for evaluating systems that search SMS conversations . the collection is built from 120,000 text messages . |
| Outcome: | The proposed test collection can be used to compare some alternative retrieval systems. |
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)
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Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang
| Challenge: | Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. |
| Approach: | They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy. |
| Outcome: | The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy. |
Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP (2021.acl-long)
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| Challenge: | Existing studies show that retrievers underperform on rarer entities that share a name . open-domain tasks require a knowledge source to perform reasoning and produce an answer . |
| Approach: | They propose an evaluation benchmark for retrieving entities that share a name . they define Ambiguous Entity Retrieval sets as a collection of entities that have a common name - and query about those entities. |
| Outcome: | The proposed sets underperform on rarer entities that share a name . the retrievers exhibit popularity bias, and are twice as likely to retrieve erroneous documents . |
Making Information Seeking Easier: An Improved Pipeline for Conversational Search (2020.findings-emnlp)
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| Challenge: | Existing tools for conversational information seeking (CIS) do not support conversational contexts. |
| Approach: | They propose a highly effective pipeline for passage retrieval in a conversational search setting using a BERT-based classifier and a multi-view reranking component. |
| Outcome: | The proposed pipeline achieves 14.8% performance improvement over the current state-of-the-art pipeline and surpasses the Oracle. |
CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions (2024.lrec-main)
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| Challenge: | CookingSense is a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes. |
| Approach: | They introduce CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes. |
| Outcome: | The proposed system improves retrieval augmented language models and food decision support systems. |
Distant Reading in Digital Humanities: Case Study on the Serbian Part of the ELTeC Collection (2022.lrec-1)
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Ranka Stanković, Cvetana Krstev, Branislava Šandrih Todorović, Dusko Vitas, Mihailo Skoric, Milica Ikonić Nešić
| Challenge: | Distant reading is a new scale of description that does not displace previous scales of literary description. |
| Approach: | They present the Serbian part of the ELTeC multilingual corpus . they propose to test various methods and tools for distant reading . |
| Outcome: | The Serbian part of the ELTeC multilingual corpus is being built to test various methods and tools . Several use examples show that this sub-collection is usefull for both close and distant reading approaches. |
Adapting General-Purpose Embedding Models to Private Datasets Using Keyword-based Retrieval (2025.findings-acl)
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| Challenge: | Text embedding models show strong performance on generic benchmarks, but their effectiveness diminishes when applied to private datasets. |
| Approach: | They propose a method for adapting general-purpose text embedding models to private datasets . they construct supervisory signals from the ranking of keyword-based retrieval results . |
| Outcome: | The proposed method improves retrieval performance across domains, datasets, and models. |
LOFT: Scalable and More Realistic Long-Context Evaluation (2025.findings-naacl)
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Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Séb Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu
| Challenge: | Long-context language models (LCLMs) can be used to perform tasks traditionally reliant on external tools like retrieval systems or databases. |
| Approach: | They propose a benchmark to evaluate LCLMs' performance on in-context retrieval and reasoning tasks using a set of tokens. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval and RAG systems on in-context retrieval tasks while still requiring prompting strategies. |
LumberChunker: Long-Form Narrative Document Segmentation (2024.findings-emnlp)
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| Challenge: | Modern NLP tasks rely on dense retrieval methods to access up-to-date and relevant contextual information. |
| Approach: | They propose a method that leverages an LLM to dynamically segment documents by iterating on a set of sequential passages to identify the point where the content begins to shift. |
| Outcome: | The proposed method outperforms the most competitive baseline by 7.37% in retrieval performance and integrates into a RAG pipeline. |
Generating Multi-Aspect Queries for Conversational Search (2026.eacl-long)
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| Challenge: | Conversational information seeking (CIS) systems aim to model the user’s information need within the conversational context and retrieve the relevant information. |
| Approach: | They propose a multi-aspect query generation and retrieval framework which uses Large Language Models to break the user utterance into multiple queries. |
| Outcome: | The proposed framework outperforms state-of-the-art query rewriting methods on six widely used CIS datasets and fine-tunes the model on MASQ yields significant improvements. |
Neural Machine Translation with Phrase-Level Universal Visual Representations (2022.acl-long)
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| Challenge: | Existing multimodal machine translation methods require paired input of source sentence and image, which makes them suffer from shortage of sentence-image pairs. |
| Approach: | They propose a phrase-level retrieval-based method to get visual information from existing sentence-image data sets. |
| Outcome: | The proposed method significantly outperforms strong baselines on multiple MMT datasets, especially when the textual context is limited. |
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. |
Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting (2023.findings-emnlp)
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| Challenge: | Existing approaches to rewrite context-dependent queries lack sufficient information for optimal retrieval performance. |
| Approach: | They propose to use large language models (LLMs) as query rewriters to generate informative queries through well-designed instructions. |
| Outcome: | The proposed approach improves performance on the QReCC dataset compared to human rewrites . |
PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements (2025.emnlp-main)
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| Challenge: | Contract review is a complex and time-intensive task that typically requires legal expertise. |
| Approach: | a new open-source contract review framework is designed to handle complexities of contract analysis . PAKTON is a retrieval-augmented generation framework with plug-and-play capabilities . |
| Outcome: | The open-source framework outperforms models in predictive accuracy, retrieval performance, explainability, completeness, and grounded justifications. |
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics (2024.lrec-main)
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Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann, Nithum Thain, Erin MacMurray van Liemt, Kathleen Meier-Hellstern, Lucas Dixon
| Challenge: | a growing audience of users is engaging with LLM-driven chatbots. |
| Approach: | They propose a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia’s Neutral Point of View principle. |
| Outcome: | The proposed methods detect errors in the tuned LLM responses even when no training data is available. |
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval (2024.naacl-long)
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| Challenge: | et al., 2020: performance of dense retrieval models in multilingual retrieval is limited due to uneven and scarce training data available across multiple languages. |
| Approach: | They propose a synthetic retrieval training dataset containing 33 languages for fine-tuning multilingual retrievers without human supervision. |
| Outcome: | The proposed model outperforms human-supervised retrieval models on three retrieval benchmarks. |
Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing (2022.coling-1)
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Yury Zemlyanskiy, Michiel de Jong, Joshua Ainslie, Panupong Pasupat, Peter Shaw, Linlu Qiu, Sumit Sanghai, Fei Sha
| Challenge: | Existing retrieval techniques for semantic parsing use similarity of query and exemplar inputs . Existing work suggests that appending training samples to training samples improves performance . |
| Approach: | They propose a retrieval procedure that retrieves exemplars for which outputs are similar . existing retrieval techniques are based on similarity of query and exemplar inputs . |
| Outcome: | Existing retrieval techniques rely on similarity of query and exemplar inputs . they retrieve exemplars with similar outputs and generate a final prediction . |
An Empirical Study of Frame Selection for Text-to-Video Retrieval (2023.findings-emnlp)
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| Challenge: | Existing methods for text-to-video retrieval select a subset of frames to represent video content . current methods only explore video contents while ignoring relevancy to texts . |
| Approach: | They propose to use a subset of frames to represent video content for TVR . they analyze six different frame selection methods to determine their effectiveness . |
| Outcome: | The proposed method improves retrieval efficiency without sacrificing visual details . the proposed method explores the video contents while ignoring relevancy to texts . |
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)
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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen
| Challenge: | Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval. |
| Approach: | They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
| Outcome: | The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models. |
Attribute or Abstain: Large Language Models as Long Document Assistants (2024.emnlp-main)
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| Challenge: | Existing approaches to attribution have only been evaluated in RAG settings, where initial retrieval confounds performance. |
| Approach: | They propose to use a benchmark to evaluate attribution on long document tasks . they find that citations and additional retrieval perform best for large models . |
| Outcome: | The proposed approach performs best on large and fine-tuned models, while additional retrieval can help for small, prompted models. |
A Statutory Article Retrieval Dataset in French (2022.acl-long)
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| Challenge: | Statutory article retrieval is the task of automatically retrieving law articles relevant to a legal question. |
| Approach: | They propose to use a Belgian Statutory Article Retrieval Dataset to test various retrieval approaches including lexical and dense architectures to achieve a 74.8% R@100. |
| Outcome: | The proposed dataset outperforms existing systems in both zero-shot and supervised setups. |
A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space (2021.emnlp-main)
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| Challenge: | Cross-lingual language models house representations for many different languages in the same space. |
| Approach: | They investigate linguistic and non-linguistic factors affecting sentence-level alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs. |
| Outcome: | The results show that word order agreement and agreement in morphological complexity are strongest predictors of cross-linguality. |
FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining (2026.acl-long)
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| Challenge: | Existing audio-language models excel at clip-level understanding but struggle with frame-level tasks. |
| Approach: | They propose a novel training paradigm that advances both clip- and frame-level alignment in CLAP with heterogeneous data. |
| Outcome: | The proposed training paradigm improves both clip- and frame-level alignment in CLAP with heterogeneous data. |
End-to-end Knowledge Retrieval with Multi-modal Queries (2023.acl-long)
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| Challenge: | a new task is proposed to learn knowledge retrieval with multimodal queries . a vision-language model can retrieve knowledge using images and text inputs . |
| Approach: | They propose a task for vision-language models to retrieve knowledge with multi-modal queries . they propose reViz, a model that integrates content from both text and image queries based on a multimodal query task . |
| Outcome: | The proposed task performs better under zero-shot settings than previous work on cross-modal retrieval. |
Distilling Large Embeddings via Hyperspherical Householder Quantization (2026.acl-long)
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| Challenge: | Existing methods for quantizing large embeddings rely on Euclidean quantization, which is poorly aligned with the angular geometry induced by contrastive embeddment training. |
| Approach: | They propose a geometry-aware distillation method that compresses large embeddings into short discrete representations via iterative Householder transformations on the unit hypersphere. |
| Outcome: | The proposed method reduces decoding cost and maintains strong semantic retrieval accuracy. |
AURORA: Neuro-Symbolic Continual Indexing for Evolving RAG Systems (2026.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems depend on non-parametric indices to access external knowledge. |
| Approach: | They propose a framework for adapting retrieval indices under distribution shift . AURORA decouples discrete index structure from continuous metric representations . it recovers +26.9% Recall@10 on novel topics compared to static baselines compared with static baseline . |
| Outcome: | AURORA decouples discrete index structure from continuous metric representations . it recovers +26.9% Recall@10 on novel topics while adapting significantly faster than full retraining. |
Simple Entity-Centric Questions Challenge Dense Retrievers (2021.emnlp-main)
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| Challenge: | Open-domain question answering has exploded in popularity due to the success of dense retrieval models. |
| Approach: | They construct a set of simple, entity-rich questions based on facts from Wikidata and test their models against supervised datasets. |
| Outcome: | The proposed model outperforms sparse retrieval methods on open-domain question answering datasets by a large margin. |
ICR: Iterative Clarification and Rewriting for Conversational Search (2025.emnlp-main)
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| Challenge: | Conversational Query Rewriting (CQR) is a key step in conversational question answering . it aims to rewrite vague queries into de-contextualized queries, thereby promoting conversational search. |
| Approach: | They propose an iterative rewriting scheme that pivots on clarification questions . they propose to rewrite queries into de-contextualized queries to promote conversational search . |
| Outcome: | The proposed framework improves retrieval performance on two popular datasets. |
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation (2025.coling-main)
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| Challenge: | Existing language models lack data and computation power, but they are extremely parameter-heavy and difficult to train. |
| Approach: | They propose a retrieval augmented generation framework backed by a large language model to correct the output of a smaller model for morphological glossing. |
| Outcome: | The proposed model is highly effective in data-scarce settings and offers a state-of-the-art for morphological glossing. |
CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval (2026.acl-long)
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Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Alexander Pretschner, Heinz Koeppl, Fakhri Karray
| Challenge: | Existing benchmarks focus on functional relevance while neglecting code quality. |
| Approach: | They propose a multilingual benchmark to evaluate quality-aware code retrieval . they include fine-grained quality annotations over 42,725 queries and 134,907 code snippets . |
| Outcome: | The proposed benchmarks show that state-of-the-art models fail to separate buggy or insecure code from robust counterparts. |
Logic Unveils Truth, While Disguise Obscures It: Transition Logic Augmented Response Selection for Multi-Turn Dialogue (2023.findings-emnlp)
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| Challenge: | Existing methods of negative samples tend to yield false negatives due to one-to-many property in open-domain dialogue. |
| Approach: | They propose a sequential variational ladder auto-encoder to capture one-to-many transition pattern of multiple characteristics in open-domain dialogue. |
| Outcome: | The proposed approach improves the performance of a retrieval dialogue system on two benchmarks. |
Synergistic Interplay between Search and Large Language Models for Information Retrieval (2024.acl-long)
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| Challenge: | Information retrieval (IR) is an indispensable technique for locating relevant resources from vast amounts of data. |
| Approach: | They propose a framework that facilitates information refinement through synergy between RMs and LLMs. |
| Outcome: | The proposed framework improves the performance of large-scale retrieval benchmarks on web searches and low-resource retrieval tasks. |
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering (2021.emnlp-main)
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| Challenge: | Existing knowledge-based visual question answering systems rely on Concept-Net and Wikipedia to obtain external knowledge. |
| Approach: | They propose a visual retriever-reader pipeline that uses a natural language knowledge base and a Visual retriever to retrieve relevant knowledge. |
| Outcome: | The proposed method significantly improves the visual retriever-reader pipeline on the OK-VQA benchmark. |
VulLibGen: Generating Names of Vulnerability-Affected Packages via a Large Language Model (2024.acl-long)
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Tianyu Chen, Lin Li, ZhuLiuchuan ZhuLiuchuan, Zongyang Li, Xueqing Liu, Guangtai Liang, Qianxiang Wang, Tao Xie
| Challenge: | Existing work on affected package identification is limited by large language models . a recent study shows that 84% third-party packages contain security vulnerabilities . |
| Approach: | They propose a method to use LLM to generate the affected package . they propose supervised fine-tuning, retrieval augmented generation and a local search algorithm . |
| Outcome: | The proposed method has an average precision of 0.806 for identifying vulnerable packages in four most popular ecosystems in GitHub Advisory. |
QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs (2024.findings-emnlp)
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| Challenge: | Existing approaches to augmented generation of retrieved passages rely on the quality of a question's retrieved information. |
| Approach: | They propose a simple yet efficient method called question and passage augmentation via LLMs for open-domain QA. |
| Outcome: | The proposed method outperforms the state-of-the-art and achieves significant performance gain over existing methods. |
SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation (2025.emnlp-main)
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Qian Dong, Jia Chen, Qingyao Ai, Hongning Wang, Haitao Li, null Yiwu, Yao Hu, Yiqun Liu, Shaoping Ma
| Challenge: | Existing retrieval-augmented code generation methods fail to accurately fetch the knowledge required for code generation for consecutive code fragments. |
| Approach: | They propose a paradigm that enables large language models to Self-express their information needs to enhance retrieval-augmented code generation methods. |
| Outcome: | Experiments show that SelfRACG can retrieve external knowledge that better aligns with the LLM’s own information needs, resulting in superior generation performance compared to vanilla RACG. |
Predicting Text Preference Via Structured Comparative Reasoning (2024.acl-long)
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Jing Nathan Yan, Tianqi Liu, Justin Chiu, Jiaming Shen, Zhen Qin, Yue Yu, Charumathi Lakshmanan, Yair Kurzion, Alexander Rush, Jialu Liu, Michael Bendersky
| Challenge: | Existing approaches to comparative reasoning rely on pretraining or fine-tuning models at the cost of massive human annotation and computation. |
| Approach: | They propose a model that prompts LLMs to generate structured intermediate comparisons by proposing aspects for comparison, followed by generating textual comparisons under each aspect. |
| Outcome: | The proposed model significantly reduces hallucination and improves consistency across various NLP tasks. |
Large Language Models Can Learn Representation in Natural Language (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are unable to complete complex tasks involving multiple entities, such as tool APIs. |
| Approach: | They propose a method which uses natural language representations to refine entity descriptions for improved retrieval and LLM utilization. |
| Outcome: | The proposed method improves GPT-4's performance on classification tasks and API call tasks. |
Communication breakdown: On the low mutual intelligibility between human and neural captioning (2022.emnlp-main)
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| Challenge: | 0-shot performance of a neural caption-based image retriever is higher when fed captions from a human-produced caption generator . despite the fact that the caption generator does not take the set of distractor images into account, this performance is only marginally above chance level. |
| Approach: | They compare the 0-shot performance of a neural caption-based image retriever with captions from a human-produced captioner. |
| Outcome: | The proposed model performs better when given human-produced captions or neural captions . the best pre-trained model perform better when fed captions produced by an out-of-the-box model . |
Improving Factuality with Explicit Working Memory (2025.acl-long)
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Mingda Chen, Yang Li, Karthik Padthe, Rulin Shao, Alicia Yi Sun, Luke Zettlemoyer, Gargi Ghosh, Wen-tau Yih
| Challenge: | Large language models can generate factually inaccurate content, a problem known as hallucination. |
| Approach: | They propose an approach that integrates a working memory that receives feedback from external resources. |
| Outcome: | The proposed method outperforms baselines on four fact-seeking datasets and increases the factuality metric by 2 to 6 points absolute. |
Momentum Posterior Regularization for Multi-hop Dense Retrieval (2025.coling-main)
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| Challenge: | Current methods for knowledge distillation in one-time retrieval are ineffective for multi-hop QA . posterior information is often defined as the response, which may not connect to the query without intermediate retrieval . |
| Approach: | They propose to distill knowledge from a posterior retrieval into a prior retrieval for multi-hop QA . they propose to use momentum moving average method to update posterior information along with prior retrievals . |
| Outcome: | Experiments on HotpotQA and StrategyQA show that MoPo outperforms baselines in retrieval and downstream QA tasks. |
Error-Robust Retrieval for Chinese Spelling Check (2024.lrec-main)
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| Challenge: | Chinese Spelling Check (CSC) aims to detect and correct spelling errors in Chinese texts . current methods may not fully leverage existing datasets, resulting in insufficient annotated data . |
| Approach: | They propose a plug-and-play retrieval method with error-robust information for Chinese Spelling Check . they employ multimodal representations that fuse phonetic, morphologic, and contextual information . |
| Outcome: | The proposed method improves on the SIGHAN benchmarks on Chinese spelling check (CSC) the proposed method is based on training data and lacks adequate parallel corpora . |
Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments (2024.acl-long)
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| Challenge: | Existing methods to verify claim credibility rely on embedded knowledge or unreliable context. |
| Approach: | They propose retrieval augmented fact verification through the synthesis of contrasting arguments (RAFTS) they use an embedding model to identify informative demonstrations and in-context prompts to generate the prediction and explanation. |
| Outcome: | The proposed method outperforms existing methods with smaller LLMs or unreliable contexts. |
Multilingual Generative Retrieval via Cross-lingual Semantic Compression (2025.findings-emnlp)
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| Challenge: | Existing methods for multilingual retrieval still face cross-lingual identifier misalignment and identifiere inflation. |
| Approach: | They propose a framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space. |
| Outcome: | The proposed framework improves cross-lingual alignment and reduces redundancy. |
TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification (2025.acl-long)
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| Challenge: | Large language models have demonstrated great potential in natural language generation, but their widespread adoption has raised concerns regarding content reliability and accountability. |
| Approach: | They propose a challenge to trace each sentence of a target text back to specific source sentences within potentially lengthy or multi-document inputs. |
| Outcome: | The proposed challenge traces each sentence of a target text back to specific source sentences . the dataset includes 11 scenarios covering QA and summarization in english and Chinese . |
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. |
Decoding a Neural Retriever’s Latent Space for Query Suggestion (2022.emnlp-main)
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Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann
| Challenge: | Neural retrieval models have replaced bag-of-words methods for document retrieval . however, they lack the interpretability of bag-off-word models . |
| Approach: | They train a query decoder that generates a meaningful query from a latent representation of a neural search engine. |
| Outcome: | The proposed model outperforms both query reformulation and PRF information retrieval baselines. |
Latent Retrieval for Weakly Supervised Open Domain Question Answering (P19-1)
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| Challenge: | Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. |
| Approach: | They propose to jointly learn the retriever and reader from question-answer string pairs and without any IR system. |
| Outcome: | The proposed approach outperforms BM25 on open datasets with a learner and reader by 19 points in exact match. |
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models (2025.acl-long)
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| Challenge: | Existing RAG frameworks rely on Automatic Speech Recognition to process speech input, which discards crucial audio information and increases computational overhead. |
| Approach: | They propose a retrieval augmented generation framework with native, end-to-end audio support that integrates audio and text into a unified knowledge representation. |
| Outcome: | The proposed framework can perform 10x faster than current pipelines while delivering 10x acceleration. |
Facilitating Long Context Understanding via Supervised Chain-of-Thought Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond. |
| Approach: | They propose a synthetic dataset in the financial domain that integrates Chain-of-Thought reasoning into LLMs in a supervised manner to facilitate effective long-context understanding. |
| Outcome: | The proposed model outperforms standard GPT-4o-mini on the Loong benchmark and fine tunes LLaMA-3.1-8B-Instruct on the model, achieving a 28.0% gain on the financial subset. |
NeoQA: Evidence-based Question Answering with Generated News Events (2025.findings-acl)
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| Challenge: | Evaluating Retrieval-Augmented Generation (RAG) in large language models is challenging because benchmarks can quickly become stale. |
| Approach: | They propose a benchmark to evaluate Retrieval-Augmented Generation (RAG) in large language models (LLMs) using timelines and knowledge bases of fictional news events and entities to prevent LLMs from leveraging pretraining knowledge. |
| Outcome: | The proposed benchmark prevents LLMs from leveraging pretraining knowledge and ensures that no prior evidence exists in their training data. |
Knowledge Corpus Error in Question Answering (2023.findings-emnlp)
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| Challenge: | Recent work in open-domain question answering (QA) has explored generating context passages from large language models (LLMs) however, it is not well understood why generated passages can be more effective than retrieved ones. |
| Approach: | They propose to generate context passages from large language models by paraphrasing human-annotated gold context using LLMs to observe knowledge corpus error. |
| Outcome: | The proposed framework shows that paraphrasing human-annotated gold contexts improves performance over retrieval steps. |
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA (2024.findings-emnlp)
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| Challenge: | Understanding users’ contextual search intent when generating responses is an understudied topic for conversational question answering (QA). |
| Approach: | They propose a method that allows LLMs to decide when to retrieve in RAG settings given a conversational context. |
| Outcome: | The proposed method improves on three conversational QA datasets and criticizes the quality of generated responses. |
STARD: A Chinese Statute Retrieval Dataset Derived from Real-life Queries by Non-professionals (2024.findings-emnlp)
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Weihang Su, Yiran Hu, Anzhe Xie, Qingyao Ai, Quezi Bing, Ning Zheng, Yun Liu, Weixing Shen, Yiqun Liu
| Challenge: | Existing statute retrieval benchmarks emphasize formal and professional queries from sources like bar exams and legal case documents . existing retrieval approaches that lack domain-specific knowledge may struggle to capture the meanings of specialized terms accurately. |
| Approach: | They propose a dataset that captures the complexity and diversity of real queries from the general public. |
| Outcome: | The proposed dataset captures the complexity and diversity of real queries from the general public. |
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal (2022.emnlp-main)
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| Challenge: | Transformer-based large language models are trained to make predictions about the next word by aggregating representations of previous tokens through their self-attention mechanism. |
| Approach: | They propose an entropy-based predictor that quantifies the diffuseness of self-attention and a distance-based one that captures the incremental change in attention patterns across timesteps. |
| Outcome: | The proposed models perform better over a rigorous baseline including GPT-2 surprisal than previous models that used entropy-based predictors and distance-based ones. |
Map of Encoders – Mapping Sentence Encoders using Quantum Relative Entropy (2026.acl-long)
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| Challenge: | a method to compare and visualise sentence encoders at scale is proposed . we map encoder LLMs using QRE-based feature vectors, which are then projected to 2D . |
| Approach: | They propose a method to compare and visualise sentence encoders at scale by creating a map of encoder . they construct a QRE-based map of sentences covering 1101 publicly available sentence encoded sentences . |
| Outcome: | The proposed method compares sentence encoders at scale by creating a map of encoder models . it shows that the map accurately reflects relationships between encoder and unit base encoder . |
CLaw: Benchmarking Chinese Legal Knowledge in Large Language Models - A Fine-grained Corpus and Reasoning Analysis (2025.findings-emnlp)
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| Challenge: | a new benchmark is designed to evaluate LLMs on Chinese legal knowledge and its application in reasoning . general pre-training that ingests legal texts without specialized focus compromises reliability of LLM responses . achieving trustworthy legal reasoning in LLM requires a robust synergy of accurate knowledge retrieval and strong general reasoning capabilities. |
| Approach: | They propose a benchmark specifically engineered to evaluate LLMs on Chinese legal knowledge and its application in reasoning. |
| Outcome: | The proposed benchmark evaluates LLMs on Chinese legal knowledge and its application in reasoning. |
CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control (2025.findings-acl)
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Liu Huanshuo, Hao Zhang, Zhijiang Guo, Jing Wang, Kuicai Dong, Xiangyang Li, Yi Quan Lee, Cong Zhang, Yong Liu
| Challenge: | Existing methods focus on detecting LLM’s confidence via statistical uncertainty. |
| Approach: | They propose to use a representation perspective to solve adaptive RAG by enabling dynamic retrieval during generation and enabling retrieval only when the query exceeds LLM's internal knowledge. |
| Outcome: | The proposed framework is superior to existing adaptive RAG methods on a diverse set of tasks. |
LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval (2026.findings-acl)
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| Challenge: | Large language models (LLMs) embeddings are typically high-dimensional, leading to substantial storage and retrieval overhead. |
| Approach: | They propose a learning-free method that transforms an LLM embedding into a binary embeddable using Isolation Kernel (IKE). |
| Outcome: | The proposed method performs 16.7 faster retrieval and 16 lower memory usage than the original LLM embeddings while maintaining comparable accuracy. |
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings. |
| Approach: | They propose a robust RAG framework for large language models via Margin-aware Preference Optimization to enhance the accuracy and reliability of SLMs. |
| Outcome: | The proposed framework surpasses state-of-the-art benchmarks on three open-domain question answering tasks. |
WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora (2026.findings-acl)
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| Challenge: | Existing benchmarks for Graph-based Retrieval-Augmented Generation (GraphRAG) rely on short, curated passages as external knowledge, failing to adequately evaluate systems in realistic settings involving long contexts and large-scale heterogeneous documents. |
| Approach: | They propose a benchmark to assess GraphRAG performance in the wild using Wikipedia's unique structure where cohesive narratives are grounded in long and heterogeneous external reference documents. |
| Outcome: | Experiments with articles across 12 top-level topics show that GraphRAG performs better in the wild than existing methods. |
Peering into the Mind of Language Models: An Approach for Attribution in Contextual Question Answering (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used for question answering . lack of explicit references or attributions hinders ability to verify accuracy of answers . |
| Approach: | They propose a method for attribution in contextual question answering . they use hidden state representations of large language models to identify copied segments . |
| Outcome: | The proposed method performs better than GPT-4 at identifying verbatim copied segments in LLM generations and attributing these segments to their source. |
Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks (2023.findings-acl)
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| Challenge: | Recent studies focus on retrieval to solve knowledge-intensive tasks, but the potential of retrieval for non-knowledge-intensive (NKI) tasks remains under-explored. |
| Approach: | They propose a task-agnostic retrieval framework for NKI tasks that uses a static index and a prompt-guided reranker to re-rank the nearest evidence according to task-specific relevance. |
| Outcome: | The proposed framework outperforms state-of-the-art retrieval-augmented methods on NKI tasks and will be released for further research. |
AFMRL: Attribute-Enhanced Fine-Grained Multi-Modal Representation Learning in E-commerce (2026.findings-acl)
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| Challenge: | Multimodal representation is crucial for E-commerce tasks such as identical product retrieval. |
| Approach: | They propose an approach which leverages the generative power of Multimodal Large Language Models to extract key attributes from product images and text and enhances representation learning through a two-stage training framework. |
| Outcome: | The proposed model achieves state-of-the-art on multiple downstream retrieval tasks, validating the effectiveness of harnessing generative models to advance fine-grained representation learning. |
Numbers Matter! Bringing Quantity-awareness to Retrieval Systems (2024.findings-emnlp)
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| Challenge: | Quantitative information is important for understanding documents and interpreting them. |
| Approach: | They propose two quantity-aware ranking techniques that rank both quantity and textual content . they use available retrieval systems to incorporate quantity information into queries . |
| Outcome: | The proposed methods can rank both quantity and textual content, either jointly or independently. |
Profiling Medical Journal Articles Using a Gene Ontology Semantic Tagger (L18-1)
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| Challenge: | a growing number of scientific publications are based on sub-divisions and sub-communities of expertise becoming disconnected from each other. |
| Approach: | They propose to examine corpora derived from bodies of genetics literature and use it to make comparisons and improve retrieval methods. |
| Outcome: | The proposed methods will help to make comparisons and improve retrieval methods using domain knowledge via an existing gene ontology. |
Analysis of Plan-based Retrieval for Grounded Text Generation (2024.emnlp-main)
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| Challenge: | Large, parametric language models (LLMs) produce fluent text for many applications . hallucinations are generation of text that is factually correct and semantically plausible . |
| Approach: | They propose to use learning-tuned LLMs to infuse models with retrieval mechanisms to reduce hallucinations. |
| Outcome: | The proposed approach reduces the frequency of hallucinations by reducing the coverage of relevant facts and generating more informative responses while providing higher attribution rates. |
CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking (2022.emnlp-main)
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| Challenge: | Contextual document embedding reranking is an efficient and efficient retrieval framework. |
| Approach: | They propose a highly efficient retrieval framework that uses contextual document embedding reranking to incorporate ranking context into training. |
| Outcome: | The proposed framework reduces the computational overhead of a first-stage method and can be used as stand-alone retrieval models. |
FreeChunker: A Cross-Granularity Chunking Framework (2026.findings-acl)
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| Challenge: | Existing chunking paradigms rely on static boundary identification, limiting performance . Existing methods rely only on static knowledge, resulting in hallucinated content . |
| Approach: | They propose a Cross-Granularity Encoding Framework that treats sentences as atomic units and shifts from static chunk segmentation to flexible retrieval supporting arbitrary sentence combinations. |
| Outcome: | The proposed framework avoids the computational overhead required for semantic boundary detection and enhances adaptability to complex queries. |
Smart-Searcher: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning (2025.findings-emnlp)
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Huatong Song, Jinhao Jiang, Wenqing Tian, Zhipeng Chen, Yuhuan Wu, Jiahao Zhao, Yingqian Min, Xin Zhao, Lei Fang, Ji-Rong Wen
| Challenge: | Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-augmented generation (RAG) helps by injecting external information, but current methods are costly, generalize poorly, or ignore the model’s internal knowledge. |
| Approach: | They propose a framework to train large language models to leverage both internal and external knowledge sources. |
| Outcome: | The proposed framework outperforms existing methods and achieves efficient retrieval-augmented reasoning. |
CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)
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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao
| Challenge: | Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality. |
| Approach: | They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation. |
| Outcome: | The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks. |
Language Concept Erasure for Language-invariant Dense Retrieval (2024.emnlp-main)
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| Challenge: | Multilingual models aim for language-invariant representations but still encode language identity. |
| Approach: | They propose a multi-task learning framework that induces language invariance in multilingual retrieval by reducing language-specific signals in the embedding space. |
| Outcome: | The proposed learning framework improves language-invariant dense retrieval over baselines on English retrieval data and general multilingual corpora. |
Tuning Less, Prompting More: In-Context Preference Learning Pipeline for Natural Language Transformation (2025.emnlp-main)
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| Challenge: | Existing approaches to natural language transformation (NLT) tasks face significant challenges, such as the computational costs of leveraging large pre-trained models and the limited generalization ability of fine-tuned smaller models. |
| Approach: | They propose a framework that combines prompting with fine-tuning to enhance smaller models by integrating In-Context Examples from retrieval. |
| Outcome: | The proposed framework outperforms existing methods across MT and TST tasks. |
Efficient Long Context Language Model Retrieval with Compression (2025.acl-long)
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| Challenge: | Long Context Language Models (LCLMs) are a new paradigm for Information Retrieval . however, processing large number of passages within in-context for retrieval is computationally expensive . a proposed compression approach for LCLM retrieval maximizes retrieval performance while minimizing the length of the compressed passages. |
| Approach: | They propose a new compression approach tailored to maximize retrieval performance while minimizing the length of compressed passages. |
| Outcome: | The proposed approach maximizes retrieval performance while minimizing the length of compressed passages while reducing the in-context size by 1.91. |
LDIR: Low-Dimensional Dense and Interpretable Text Embeddings with Relative Representations (2025.findings-acl)
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| Challenge: | Existing text embeddings with high dimensions are difficult to trace and interpret. |
| Approach: | They propose low-dimensional and interpretable text embeddings with relative representations that encode semantic meanings in a vector space where similar texts are close together in the representation space. |
| Outcome: | The proposed embeddings outperform existing models on multiple tasks with fewer dimensions and are lowdimensional and dense while maintaining interpretability. |
D2-RAG: Dual-Decision Retrieval-Augmented Generation via Multi-Dimensional Uncertainty and Utility-Aware Decoding (2026.findings-acl)
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Jinshuo Zhang, Xiaoding Zhou, Weiyu Zhang, Guoqiang Chen, Ying Lian, Xiaoyang Meng, Yonghe Chen, Hongjiao Guan, Jiasheng Si, Wenpeng Lu
| Challenge: | Retrieval-Augmented Generation (RAG) mitigates hallucinations in large language models by incorporating external knowledge. |
| Approach: | They propose a dual-decision retrieval-augmented generation that integrates multi-dimensional uncertainty estimation to decide whether to retrieve and employs adaptive contrastive decoding to handle retrieved contexts of varying quality. |
| Outcome: | The proposed model outperforms baselines on four medical question-answering datasets while suppressing interference from noisy contexts. |
Exploring the Practicality of Generative Retrieval on Dynamic Corpora (2024.emnlp-main)
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| Challenge: | a lack of comprehensive comparison between GR and Dual Encoders in IR systems is limiting . GR is more adaptable to evolving knowledge (4–11%), robust in learning knowledge with temporal information, and efficient in terms of inference FLOPs (x2), indexing time (x6) and storage footprint (x4) |
| Approach: | They propose to use autoregressive language models to perform information retrieval (IR) their results highlight the potential of GR for future use in practical IR systems . |
| Outcome: | The proposed model is more adaptable to evolving knowledge (4–11%), robust in learning knowledge with temporal information, efficient inference FLOPs (x2), indexing time (x6), and storage footprint (x4) compared to the most common model, Dual Encoder (DE). |
CoEvo: Coevolution of LLM and Retrieval Model for Domain-Specific Information Retrieval (2025.emnlp-main)
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Ang Li, Yiquan Wu, Yinghao Hu, Lizhi Qing, Shihang Wang, Chengyuan Liu, Tao Wu, Adam Jatowt, Ming Cai, Fei Wu, Kun Kuang
| Challenge: | Recent methods to enhance queries by generating intermediary elements can degrade retrieval performance . combining LLMs and retrievers can be difficult, resulting in unreliable or irrelevant intermediaries . |
| Approach: | They propose a framework that facilitates the coevolution of large language models and retrieval models. |
| Outcome: | The proposed framework facilitates the coevolution of LLMs and retrieval models. |
Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context (2026.acl-long)
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Yilun Zhu, Yuan Zhuang, Nikhita Vedula, Dushyanta Dhyani, Shaoyuan Xu, Mohsen Bayati, Bryan Wang, Shervin Malmasi
| Challenge: | Existing methods for text regression lack local grounding and rely on shared representations. |
| Approach: | They propose a distributional regression model with quantile tokens that insert dedicated quantiles into the input sequence. |
| Outcome: | The proposed method outperforms baseline models on the inside Airbnb and StackSample datasets. |
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points . dense retrievers are limited by their inability to perform semantic matching for relevant passages that have low lexical overlap with the query. |
| Approach: | They propose a query expansion and reranking approach for improving passage retrieval with the application to open-domain question answering. |
| Outcome: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points when compared to a vanilla query expansion model and a dense retrieval model. |
M3Retrieve: Benchmarking Multimodal Retrieval for Medicine (2025.emnlp-main)
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| Challenge: | Strong retrieval models are increasingly important in knowledge-intensive domains. |
| Approach: | They propose a benchmark to evaluate multimodal retrieval models in medical settings . they examine 1.2 million text documents and 164K multimodal queries . |
| Outcome: | The proposed model spans 5 domains,16 medical fields, and 4 distinct tasks with over 1.2 Million text documents and 164K multimodal queries. |
QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations (2023.acl-long)
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| Challenge: | People express information needs with multiple preferences or constraints . modern retrieval systems struggle on such queries, a study finds . |
| Approach: | They construct a dataset of 3357 queries that map to a set of Wikipedia entities . they use crowd-sourced data to match constraints with evidence in documents . |
| Outcome: | The proposed dataset challenges models to match constraints mentioned in queries with evidence in documents and correctly perform various set operations. |
OntologyRAG-Q: Resource Development and Benchmarking for Retrieval-Augmented Question Answering in Qur’anic Tafsir (2025.emnlp-main)
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| Challenge: | An annotated Tafsir ontology and a collection of 15 structured Tafsian books are presented in this paper. |
| Approach: | They propose a framework for retrieval and question-answering Tafsir data that spans the entire pipeline from dataset construction through evaluation and error analysis. |
| Outcome: | The proposed framework achieves 69.52% accuracy and 74.36% correctness overall, though multi-hop and context-dependent questions remain challenging. |
DataSeer: A Manager-Centric Collaborative Multi-Agent Framework with Multi-Branch Reasoning for Automated Insight Discovery (2026.findings-acl)
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| Challenge: | Existing methods for automated insight discovery lack contextual coherence and coverage due to single-path exploration. |
| Approach: | They propose a Manager-Centric Collaborative Framework that integrates planner and executor . it ensures cross-episode contextual coherence and allows for adaptive sub-goal generation . |
| Outcome: | The proposed framework outperforms baselines on InsightBench and Inseval. |
CLAG: Adaptive Memory Organization via Agent-Driven Clustering for Small Language Model Agents (2026.findings-acl)
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| Challenge: | Large language model agents rely on external memory to support knowledge reuse and reasoning tasks. |
| Approach: | They propose a CLustering-based AGentic memory framework where an agent actively organizes memory . they employ an SLM-agent driven router to assign each new memory to a semantically coherent cluster . |
| Outcome: | The proposed framework improves answer quality and robustness over previous memory systems. |
TextGenSHAP: Scalable Post-Hoc Explanations in Text Generation with Long Documents (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are difficult to explain and understand due to long input contexts and autoregressive output generation. |
| Approach: | They propose a post-hoc explanation method which incorporates LLM-specific techniques. |
| Outcome: | The proposed method improves retrieval recall and prediction accuracy significantly on open-domain question answering benchmarks. |
Database-Augmented Query Representation for Information Retrieval (2025.emnlp-main)
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| Challenge: | Information retrieval models that aim to search for documents relevant to a query have shown multiple successes, but the query from the user is oftentimes short, which challenges the retrievers to correctly fetch relevant documents. |
| Approach: | They propose a database-augmented Query representation framework which augments the query with various (query-related) metadata across multiple tables. |
| Outcome: | The proposed framework significantly improves overall retrieval performance over baselines. |
Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering (2025.findings-acl)
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Binquan Ji, Haibo Luo, YifeiLu YifeiLu, Lei Hei, Jiaqi Wang, Tingjing Liao, Wang Lingyu, Shichao Wang, Feiliang Ren
| Challenge: | Existing approaches to solve multi-hop question answering challenges require multiple rounds of retrieval and iterative generation. |
| Approach: | They propose a framework that decomposes complex questions into coherent subquestions . it then iteratively refines these subquests through context-aware rewriting to generate effective query formulations. |
| Outcome: | The proposed framework performs on par with or surpasses state-of-the-art benchmarks while significantly reducing token consumption. |
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation (2025.findings-acl)
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Mohammad Mahdi Abootorabi, Amirhosein Zobeiri, Mahdi Dehghani, Mohammadali Mohammadkhani, Bardia Mohammadi, Omid Ghahroodi, Mahdieh Soleymani Baghshah, Ehsaneddin Asgari
| Challenge: | Large Language Models (LLMs) suffer from hallucinations and outdated knowledge due to their reliance on static training data. |
| Approach: | They review training strategies, robustness enhancements, loss functions, and agent-based approaches and outline open challenges and future directions to guide research in this evolving field. |
| Outcome: | The proposed model improves accuracy and accuracy while integrating external dynamic information for improved factual grounding. |
CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL (2023.emnlp-main)
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| Challenge: | Existing Text-to-SQL generators require the entire schema to be encoded with the user text. |
| Approach: | They propose a method that uses an LLM to hallucinate a minimal DB schema . they use the hallucinated schema to retrieve a subset of the actual schema based on multiple dense retrievals . |
| Outcome: | The proposed method leads to significantly higher recall than existing methods. |
Few-shot Reranking for Multi-hop QA via Language Model Prompting (2023.acl-long)
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| Challenge: | Existing methods for multi-hop QA with open-domain questions require a large number of labeled question-document pairs for retrieval. |
| Approach: | They propose a language-based prompt for multi-hop path reranking that relies on language model prompting to generate a relevance score between a question and the path. |
| Outcome: | The proposed method yields strong retrieval performance on HotpotQA with only 128 training examples compared to state-of-the-art methods trained on thousands of examples. |
GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models (2025.findings-emnlp)
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Jialin Chen, Houyu Zhang, Seongjun Yun, Alejandro Mottini, Rex Ying, Xiang Song, Vassilis N. Ioannidis, Zheng Li, Qingjun Cui
| Challenge: | Existing graph RAGs decouple retrieval and reasoning processes, preventing adaptability . existing graph Raggings depend heavily on ground-truth entities, which are often unavailable in open-domain settings. |
| Approach: | They propose a graph retriever that is trained end-to-end with large-scale graphs . structure and semantic features are encoded via soft tokens and the verbalized graph . |
| Outcome: | The proposed approach improves the performance of large-scale graph retrieval models by grounding it with external knowledge. |
Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation (2026.acl-long)
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Peiru Yang, Haoran Zheng, Tong Ju, Shiting Wang, Wanchun Ni, Jiajun Liu, Shangguang Wang, Yongfeng Huang, Tao Qi
| Challenge: | Existing studies have investigated knowledge poisoning attacks in medical RAG systems . knowledge poison attacks can disrupt model outputs and undermine system reliability . |
| Approach: | They propose a knowledge poisoning framework that injects misinformation into textual data . they propose to use paired visual data as a query-agnostic trigger to promote retrieval . |
| Outcome: | The proposed framework produces clinically plausible but incorrect generations on five LLMs and datasets. |
Bridging Intuitive Associations and Deliberate Recall: Empowering LLM Personal Assistant with Graph-Structured Long-term Memory (2025.findings-acl)
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| Challenge: | Large language models (LLMs)-based personal assistants struggle to capture entity relationships and handle multiple intents effectively. |
| Approach: | They propose a graph-structured memory framework that mimics human cognitive processes and an event-centric memory graph. |
| Outcome: | The proposed framework outperforms retrieval and QA methods across long-term dialogue benchmarks and enables more human-like memory systems. |
LexDrafter: Terminology Drafting for Legislative Documents Using Retrieval Augmented Generation (2024.lrec-main)
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| Challenge: | With the increase in legislative documents, the number of new terms and their definitions is increasing as well. |
| Approach: | They propose a framework that helps in drafting Definitions articles for legislative documents using retrieval augmented generation and existing term definitions present in different legislative documents. |
| Outcome: | The proposed framework can be used to draft Definitions articles for legislative documents using retrieval augmented generation and existing term definitions present in different legislative documents. |
Generative Frame Sampler for Long Video Understanding (2025.findings-acl)
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| Challenge: | Existing video large language models (LMMs) employ an impedance of thousands of frames to understand long videos. |
| Approach: | They propose a plug-and-play module integrated with VideoLLMs to facilitate efficient lengthy video perception. |
| Outcome: | The proposed module boosts the performance of open-source VideoLLMs and proprietary assistants on long-form video benchmarks. |
Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA (2024.findings-emnlp)
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| Challenge: | Recent studies suggest that Knowledge Graphs (KGs) contain valuable external knowledge for LLMs. |
| Approach: | They propose to model a conditional subgraph retrieval task handled by small language models and use a subgraph identifier as a special token to retrieve subgraphs. |
| Outcome: | The proposed model achieves competitive retrieval performance compared to state-of-the-art models relying on 7B parameters. |
MegaPairs: Massive Data Synthesis for Universal Multimodal Retrieval (2025.acl-long)
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Junjie Zhou, Yongping Xiong, Zheng Liu, Ze Liu, Shitao Xiao, Yueze Wang, Bo Zhao, Chen Jason Zhang, Defu Lian
| Challenge: | despite the growing demand for multimodal retrieval, there is a lack of training data. |
| Approach: | They propose a data synthesis method that leverages vision language models and open-domain images to generate high-quality data. |
| Outcome: | The proposed method outperforms baseline models on 70 more datasets and can scale up. |
ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing approaches to Personalized Retrieval-Augmented Generation (RAG) ignore long-term user information and inter-user relationships when constructing retrieval contexts, limiting personalization and the ability to leverage analogous users' knowledge for improved generation quality. |
| Approach: | They propose a Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation that organizes users into semantically coherent clusters and performs retrieval at both the cluster and document levels via cluster-level similarity and fine-grained ranking. |
| Outcome: | Extensive experiments on the LaMP benchmark show that ClusterRAG integrates seamlessly with different dense retrievers and rankers, and remains effective when paired with both fine-tuned and zero-shot language models. |
StruNRAG: Evaluation of OCR-Induced Structural Noise on RAG Robustness (2026.findings-acl)
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| Challenge: | Existing evaluations of RAG systems ignore structural noise, authors say . complex layouts can cause OCR failures and disrupt semantic flow of text . advanced LLMs demonstrate robustness against local noise, but struggle to maintain reasoning capabilities under severe structural disruption that fragments global context. |
| Approach: | They propose a benchmark to evaluate RAG robustness against OCR-induced structural perturbations. |
| Outcome: | The proposed benchmark systematically injects three categories of real-world structural noise into a bilingual dataset of 2,132 question-answer pairs . results show that advanced LLMs demonstrate robustness against local noise, but struggle to maintain reasoning capabilities under severe structural disruption . |
ThinkQE: Query Expansion via an Evolving Thinking Process (2025.findings-emnlp)
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| Challenge: | LLM-based methods often generate narrowly focused expansions that overlook these desiderata. |
| Approach: | They propose a test-time query expansion framework that promotes exploration and result diversity . ThinkQE encourages deeper and comprehensive semantic exploration and a corpus-interaction strategy that iteratively refines expansions . |
| Outcome: | The proposed framework outperforms prior approaches on diverse web search benchmarks. |
CF-TCIR: A Compositor-Free Framework for Hierarchical Text-Conditioned Image Retrieval (2024.findings-acl)
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| Challenge: | Existing approaches to text-conditioned image retrieval use attention-driven compositors . instead, we reformulate the retrieval process as a cross-modal interaction between a synthesized image feature and its corresponding text descriptor. |
| Approach: | They propose a compositor-free framework for text-conditioned image retrieval . they propose combining a reference image and modification text to form a query tuple . |
| Outcome: | The proposed framework offers advantages in terms of computational efficiency, scalability, and performance. |
GAMIC: Graph-Aligned Molecular In-context Learning for Molecule Analysis via LLMs (2025.findings-emnlp)
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| Challenge: | Current methods for retrieving large language models rely on molecule feature similarity, such as Morgan fingerprints, which do not adequately capture the global molecular and atom-binding relationships. |
| Approach: | They propose a self-supervised learning technique that embeds demonstration examples into the input prompt. |
| Outcome: | The proposed technique outperforms simple Morgan-based retrieval methods across tasks by up to 45%. |
Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings (2025.findings-acl)
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Yubo Ma, Jinsong Li, Yuhang Zang, Xiaobao Wu, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Haodong Duan, Jiaqi Wang, Yixin Cao, Aixin Sun
| Challenge: | Visualized Document Retrieval (VDR) uses large vision-language models to encode document pages into embeddings. |
| Approach: | They evaluate methods to reduce patch embeddings per page while minimizing performance degradation. |
| Outcome: | The proposed method maintains 98.2% of retrieval performance with only 11.8% of original memory usage and preserves 94.6% effectiveness at 2% memory footprint. |
ConceptCarve: Dynamic Realization of Evidence (2025.acl-long)
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| Challenge: | ConceptCarve is a social media-based evidence retrieval framework that enables users to analyze complex thought patterns that manifest differently across social media communities. |
| Approach: | They propose ConceptCarve, an evidence retrieval framework that utilizes traditional retrieval systems and LLMs to dynamically characterize the search space during retrieval. |
| Outcome: | The proposed framework surpasses traditional retrieval systems in finding evidence within a social media community and produces an interpretable representation of the evidence for that community. |
Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing approaches to online claim verification rely on prompt engineering or pre-designed reasoning workflows. |
| Approach: | They propose an online reinforcement learning framework that enables an LLM to interact with a search engine and receive reward signals that explicitly shape its planning, retrieval, and reasoning behaviors. |
| Outcome: | Empirical results show that Veri-R1 improves joint accuracy by 30% and doubles evidence score, often surpassing larger-scale model counterparts. |
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA (2026.acl-long)
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| Challenge: | Existing methods to improve the reliability of Large Language Models (LLMs) in clinical applications require factual knowledge from open-ended datasets and clinical case-based knowledge to provide context grounded in real-world patient experiences. |
| Approach: | They propose a retrieval-augmented generation framework based on the electronic health record to offer contextual information from other patients’ discharge reports. |
| Outcome: | The proposed framework outperforms a text-based ranker in a clinical QA dataset with 1,280 discharge-related questions . |
MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding (2026.findings-acl)
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| Challenge: | Existing methods for video retrieval rely on embedding-based full-corpus scanning, but there is a bottleneck in semantic asymmetry and computational redundancy. |
| Approach: | They propose a multi-agent framework that rethinks retrieval as cooperative reasoning . they parse raw videos into a structured semantic library, enabling explicit attribute-level indexing . |
| Outcome: | The proposed framework bridges the granularity mismatch gap by parsing raw videos into a structured semantic library . it employs a Logic-aware Debate mechanism with a strict veto protocol . the proposed framework achieves competitive performance without task-specific fine-tuning . |
s3: You Don’t Need That Much Data to Train a Search Agent via RL (2025.emnlp-main)
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| Challenge: | Existing approaches to optimize retrieval using search-only metrics ignore downstream utility and fine-tune entire LLM to jointly reason and retrieve limit retrieval utility and compatibility with frozen or proprietary models. |
| Approach: | They propose a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the search user using a Gain Beyond RAG reward. |
| Outcome: | The proposed framework outperforms baselines trained on over 70 more data with 2.4k training samples. |
VideoRAG: Retrieval-Augmented Generation over Video Corpus (2025.findings-acl)
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| Challenge: | Existing approaches to generating models rely on text and images, but video content is a rich source of multimodal knowledge. |
| Approach: | They propose a framework that dynamically retrieves videos based on their relevance with queries . they use large video language models to represent video content for retrieval . |
| Outcome: | The proposed framework retrieves videos based on relevance with queries and integrates both visual and textual information. |
RPDR: A Round-trip Prediction-Based Data Augmentation Framework for Long-Tail Question Answering (2025.emnlp-main)
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| Challenge: | Long-tail question answering presents significant challenges for large language models due to limited ability to acquire and accurately recall less common knowledge. |
| Approach: | They propose a data augmentation framework that selects high-quality easy-to-learn training data to enhance dense retrieval models. |
| Outcome: | The proposed framework improves on two long-tail retrieval benchmarks, PopQA and EntityQuestion, and shows that it outperforms existing retrievers on extremely long-tailed questions. |
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026.acl-long)
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Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong
| Challenge: | Existing agentic systems are retrieval-heavy but reasoning-light . current systems lack compositional reasoning, a key component of deep research . |
| Approach: | They propose a data synthesis pipeline WebAggregator to shift agentic paradigm . they use Proactive Explorer to collect interconnected knowledge and Compositional Logic Proposer to weave knowledge into complex questions . |
| Outcome: | The proposed pipeline surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench. |
Tracing and Dissecting How LLMs Recall Factual Knowledge for Real World Questions (2025.acl-long)
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| Challenge: | Recent advances in large language models have shown promising ability to perform commonsense reasoning. |
| Approach: | They propose a two-dimensional analysis framework that incorporates token back-tracing and token decoding to uncover how LLMs conduct factual knowledge recall. |
| Outcome: | The proposed framework shows that LLMs lack relevant knowledge but struggle to select the most accurate information based on context during the retrieval and rerank phase. |
MED-COREASONER: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning (2026.acl-long)
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Fan Gao, Sherry T. Tong, Jiwoong Sohn, Jiahao Huang, Junfeng Jiang, Ding Xia, Piyalitt Ittichaiwong, Kanyakorn Veerakanjana, Hyunjae Kim, Qingyu Chen, Edison Marrese-Taylor, Kazuma Kobayashi, Akiko Aizawa, Irene Li
| Challenge: | Existing models that use English and local languages have a multilingual gap . a language-informed co-reasoning framework can be used to improve multilingual reasoning . |
| Approach: | They propose a language-informed co-reasoning framework that elicits parallel English and local-language reasoning and abstracts them into structured concepts. |
| Outcome: | Experiments show that Med-CoReasoner improves multilingual reasoning performance by 5% . the framework produces clinically sound and culturally grounded reasoning traces . |
Probe Then Retrieve and Reason: Distilling Probing and Reasoning Capabilities into Smaller Language Models (2024.lrec-main)
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| Challenge: | Recent research efforts have focused on distilling Large Language Models into Small Language Model (SLMs) however, the results of CoT distillation are inadequate for knowledge-intensive reasoning tasks. |
| Approach: | They propose a retrieval-based framework which distills question probing and reasoning capabilities from Large Language Models into SLMs. |
| Outcome: | The proposed framework improves probing and reasoning capabilities of large language models in knowledge-intensive reasoning tasks. |
Beyond Function-Level Search: Repository-Aware Dual-Encoder Code Retrieval with Adversarial Verification (2025.findings-emnlp)
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| Challenge: | escalating complexity of modern codebases has intensified the need for code retrieval systems capable of interpreting cross-component change intents. |
| Approach: | RepoAlignBench is a benchmark designed to evaluate repository-level code retrieval . the benchmark proposes an adversarial reflection-augmented dual-tower architecture . |
| Outcome: | The proposed framework achieves 12.2% Top-5 Accuracy and 7.1% Recall improvements over state-of-the-art benchmarks. |
MS-RAG: Simple and Effective Multi-Semantic Retrieval-Augmented Generation (2025.emnlp-main)
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| Challenge: | Existing methods for large language models suffer from poor indexing and inference speed . graph-based RAGs heavily rely on LLM for retrieval thus inference slow . |
| Approach: | They propose retrieval-augmented generation (RAG) which integrates knowledge with dense vectors to build a multi-semantic RAG. |
| Outcome: | The proposed method achieves state-of-the-art performance with faster inference speed compared to existing methods . |
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Existing RAG frameworks face critical limitations due to text chunking and semantic similarity. |
| Approach: | They propose a framework that incorporates causal graphs into the retrieval process. |
| Outcome: | The proposed framework preserves contextual continuity and improves retrieval precision, leading to more accurate and interpretable responses. |
MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model (2025.findings-emnlp)
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| Challenge: | Existing molecule-text models ignore complementary information in different molecular views and rely on single-view representations, limiting molecule structural understanding. |
| Approach: | They propose a framework that aligns multi-view molecular representations into a unified textual space using a multi-querying transformer. |
| Outcome: | The proposed framework ensures cross-view consistency while the proposed token-level contrastive loss preserves diverse molecular features across textual queries. |
ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering (2024.emnlp-main)
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| Challenge: | Current Large Language Models (LLMs) have shown strong reasoning capabilities in commonsense question answering benchmarks, but the process underlying their success remains largely opaque. |
| Approach: | They propose a zero-shot question answering framework that combines retrieval, case-based reasoning and introspection to improve the model's performance and interpretability. |
| Outcome: | The proposed framework outperforms existing LLMs and previous knowledge integration approaches in commonsense reasoning benchmarks and achieves an average accuracy improvement of 4.5 points. |
Your LLM Agent Can Leak Your Data: Data Exfiltration via Backdoored Tool Use (2026.findings-acl)
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| Challenge: | Existing studies have not examined how backdoored agents can influence tool-use sequences to perform harmful actions. |
| Approach: | They propose a backdoor attack framework that embeds semantic triggers into fine-tuned LLM agents. |
| Outcome: | The proposed framework embeds semantic triggers into fine-tuned LLM agents . when triggered, the backdoored agent invokes memory-access tool calls to retrieve stored user context and exfiltrates it via disguised retrieval tool calls. |
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)
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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen
| Challenge: | Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy. |
| Approach: | They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges. |
| Outcome: | The proposed framework reduces retrieval time while maintaining high model performance. |
Rerank Before You Reason: Analyzing Reranking Tradeoffs through Effective Token Cost in Deep Search Agents (2026.findings-acl)
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| Challenge: | Recent work emphasizes improving efficiency in LLM-based systems, especially for longcontext and multi-step reasoning. |
| Approach: | They analyze the role of listwise reranking in deep search pipelines and compare their results to a novel ETC metric to determine model scale and reasoning effort. |
| Outcome: | The proposed model scale, reasoning effort, reranking depth, and total token cost (ETC) metric improve retrieval and end-to-end accuracy and moderate reranked agents achieve comparable accuracy at substantially lower cost. |
Mina: A Multilingual LLM-Powered Legal Assistant Agent for Empowering Access to Justice in Bangladesh (2026.findings-acl)
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| Challenge: | Existing AI legal assistants lack Bengali-language support and jurisdiction-specific adaptation, limiting their effectiveness. |
| Approach: | They developed a multilingual LLM-based legal assistant tailored for the Bangladeshi context that employs multilingual embeddings and a RAG-based chain-of-tools framework for retrieval, reasoning, translation, and document generation. |
| Outcome: | Evaluated by law faculty from leading Bangladeshi universities across all stages of the 2022 and 2023 Bangladesh Bar Council examinations, Mina achieved scores of 75–80% in preliminary MCQs, written, and simulated viva voce components. |
FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)
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Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo
| Challenge: | Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment. |
| Approach: | They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling . |
| Outcome: | The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines. |
AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels (2025.findings-emnlp)
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| Challenge: | Effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data. |
| Approach: | They propose a framework that leverages large language models to generate hypothetical documents . they also propose 'CMIRB' to provide a rigorous evaluation suite . |
| Outcome: | The proposed framework outperforms HyDE in retrieval accuracy and generalization . it leverages large language models to generate hypothetical documents conditioned on a query . |
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation (2025.acl-long)
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| Challenge: | Adaptive Retrieval-Augmented Generation (RAG) is an effective strategy to alleviate hallucination of large language models (LLMs). |
| Approach: | They propose a novel adaptive RAG model that extracts self-aware uncertainty of large language models from their internal states and invokes retrieval accordingly. |
| Outcome: | The proposed model outperforms existing adaptive RAG methods on complex and simple Question Answering datasets. |
From RAG to Agentic RAG for Faithful Islamic Question Answering (2026.findings-acl)
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Gagan Bhatia, Hamdy Mubarak, Mustafa Jarrar, George Mikros, Fadi Zaraket, Mahmoud Alhirthani, Mutaz al-Khatib, Logan Cochrane, Kareem Mohamed Darwish, Rashid Yahiaoui, Firoj Alam
| Challenge: | Large Language Models (LLMs) are increasingly used for Islamic question answering, where ungrounded responses may carry serious religious consequences. |
| Approach: | They propose a bilingual, bilingual, Arabic/English benchmark with atomic single-gold answers that measures hallucination and abstention. |
| Outcome: | The proposed model improves accuracy and robustness even with a small model. |
Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings (2025.findings-acl)
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| Challenge: | Prompt-based text embedding models generate task-specific embeddables but have thousands of dimensions . dimensionality reductions for embedded text can result in performance degradations of only the first 25% of the dimensions resulting in a very small degradation . |
| Approach: | They investigate how post-hoc dimensionality reduction affects performance of various tasks . they find that embeddings for classification and clustering exhibit lower intrinsic dimensionalities . |
| Outcome: | The proposed model generates task-specific embeddings upon receiving tailored prompts, but has thousands of dimensions and high storage costs. |
When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR (2025.findings-acl)
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Dayoon Ko, Jinyoung Kim, Sohyeon Kim, Jinhyuk Kim, Jaehoon Lee, Seonghak Song, Minyoung Lee, Gunhee Kim
| Challenge: | Dense retrievers encode text into embeddings to retrieve relevant documents . however, real-world corpora evolve, resulting in degraded retrieval performance . identifying when a dense retriever requires an update is critical for robust retrieval systems . |
| Approach: | They propose a task of predicting whether a corpus is out-of-distribution (OOD) relative to a dense retriever before indexing. |
| Outcome: | The proposed method detects whether a corpus is out-of-distribution (OOD) relative to a dense retriever before indexing. |
Sightation Counts: Leveraging Sighted User Feedback in Building a BLV-aligned Dataset of Diagram Descriptions (2025.acl-long)
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| Challenge: | Existing studies show that direct generation of diagram descriptions is costly and biased against blind and low-vision (BLV) users. |
| Approach: | They ask sighted individuals to assess diagram descriptions generated by vision-language models . they use latent supervision to guide the models with latent inference . |
| Outcome: | The results show that visual descriptions generated by vision-language models are effective and useful to educators who are themselves BLV and teach visually impaired learners. |
PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder (2025.acl-long)
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| Challenge: | Existing embedding models excel at capturing general meaning, but overlook ideological nuances, limiting their effectiveness in political bias tasks. |
| Approach: | They propose a framework to Produce inteRpretable polItical biaS eMbeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art embedding models in political bias classification . the proposed framework offers highly interpretable representations for political analysis . |
Sticking to the Mean: Detecting Sticky Tokens in Text Embedding Models (2025.acl-long)
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| Challenge: | Sticky tokens, when repeatedly inserted into sentences, pull sentence similarity toward a certain value, disrupting the normal distribution of embedding distances and degrading downstream performance. |
| Approach: | They propose a method to detect “sticky tokens” by sentence and token filtering and apply it to 40 checkpoints across 14 model families. |
| Outcome: | The proposed method detects 868 sticky tokens across 14 models and shows that their presence does not correlate with model size or vocabulary size. |
Memorizing is Not Enough: Deep Knowledge Injection Through Reasoning (2025.acl-long)
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Ruoxi Xu, Yunjie Ji, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Ben He, Yingfei Sun, Xiangang Li, Le Sun
| Challenge: | Existing knowledge injection frameworks focus on knowledge memorization and retrieval, but static nature of large language models leads to outdated information as the real world evolves or when adapting to domain-specific knowledge. |
| Approach: | They propose a four-tier knowledge injection framework that defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. |
| Outcome: | The proposed framework defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. |
TALON: A Multi-Agent Framework for Long-Table Exploration and Question Answering (2025.emnlp-main)
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| Challenge: | Existing approaches to query-relevant content retrieval fail to retrieve contextually relevant data. |
| Approach: | They propose a multi-agent framework for table question answering over long tables . TALON features a planning agent that iteratively invokes a tool agent to access tabular data . |
| Outcome: | The proposed framework achieves average accuracy improvements of 7.5% and 12.0% across all language models. |
Randomly Removing 50% of Dimensions in Text Embeddings has Minimal Impact on Retrieval and Classification Tasks (2025.emnlp-main)
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| Challenge: | Existing studies on text embeddings focus less on how information is encoded. |
| Approach: | They find that truncating embedding dimensions causes an increase in performance when removed. |
| Outcome: | The proposed method improves performance across 6 state-of-the-art text encoders and 26 downstream tasks. |
BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering (2025.emnlp-main)
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Costas Mavromatis, Soji Adeshina, Vassilis N. Ioannidis, Zhen Han, Qi Zhu, Ian Robinson, Bryan Thompson, Huzefa Rangwala, George Karypis
| Challenge: | Existing approaches to knowledge graph question answering (KGQA) rely on Large Language Model (LLM) agents for graph traversal and retrieval. |
| Approach: | They propose a framework that synergizes Large Language Models with specialized graph retrieval tools to enhance KGQA. |
| Outcome: | The proposed framework outperforms the second-best graph retrieval method by 4.5% points while showing better generalization to custom KGs. |
Towards semantic reliable clinical QA: Query pipeline optimization for cancer patient question answering systems (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are promising for medical Question-Answering but suffer from hallucinations that jeopardize patient safety. |
| Approach: | They propose a three-level controllable metadata-aware framework optimized for Cancer Patient QA (CPQA) they propose combining semantic retrieval with real-time Boolean search to overcome metadata blindness. |
| Outcome: | The proposed framework improves the answer accuracy of Claude-3-haiku by 5.24% over chain-of-thought prompting and about 3% over a naive RAG setup. |
When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval (2026.acl-long)
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| Challenge: | Existing multilingual retrievers are largely untested for mixed language queries . sensitivity of dense retrievers to mixed language querying remains poorly understood . |
| Approach: | They propose to use mixed queries as an interpolation of monolingual embeddings to evaluate retrieval performance. |
| Outcome: | The proposed model outperforms the best monolingual endpoint in 88/105 cases. |
Typos Correction Training against Misspellings from Text-to-Text Transformers (2024.lrec-main)
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| Challenge: | Existing dense retrieval systems suffer from typoed queries due to mistyping or phonetic typing errors. |
| Approach: | They propose a method that incorporates the spelling correction objective into the DR model and a prompt-based augmentation technique to enhance the alignment of the typoed query and its original query. |
| Outcome: | The proposed model outperforms existing typos-aware training approaches and sophisticated training advanced retrievers. |
Real-time Ad Retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising (2025.emnlp-main)
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| Challenge: | Existing methods for retrieving documents and ads use one-to-few mappings and time-consuming content extraction. |
| Approach: | They propose a framework that leverages LLM-generated commercial intents as an intermediate semantic representation to directly retrieve ads for queries in real-time. |
| Outcome: | The proposed framework has been implemented in a real-world online system, handling daily search volumes in billions. |
The Retrieval Bottleneck: Scaling Laws for Reinforcement Learning in RAG (2026.acl-long)
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| Challenge: | Retrieval-augmented generation (RAG) has become the dominant paradigm for building knowledge-intensive language systems. |
| Approach: | They propose a sigmoidal scaling law that shows that retrieval quality determines the asymptotic performance ceiling. |
| Outcome: | The proposed model achieves strong performance on knowledge-intensive benchmarks while retaining the predictable scaling long available for pre-training but previously absent in RAG-RL. |
Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval (2024.lrec-main)
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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
| Challenge: | Existing methods for video-text retrieval capture fine-grained semantic concepts . however, they lack the ability to capture finer-grain concepts such as objects and actions. |
| Approach: | They propose a dual-encoder architecture for fast video-text retrieval that learns lexicon representations to capture fine-grained semantics. |
| Outcome: | The proposed framework outperforms existing methods with 4.8% and 8.2% improvement on MSR-VTT and DiDeMo respectively. |
RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing graph-based or hybrid systems lack the ability to integrate supplementary evidence as reasoning unfolds. |
| Approach: | They propose a framework that integrates non-parametric knowledge into Large Language Models . they use a RL-based framework to optimize the entire generation process via RL . |
| Outcome: | The proposed framework outperforms existing RAG frameworks in five question answering benchmarks. |
Visual-Linguistic Dependency Encoding for Image-Text Retrieval (2024.lrec-main)
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| Challenge: | Existing approaches to image-text retrieval ignore semantic discrepancies caused by syntactic structure in natural language expressions and relationships among visual entities. |
| Approach: | They propose a visual-linguistic dependency encoder framework which explicitly models the dependency information among textual words and interaction patterns between image regions. |
| Outcome: | The proposed framework outperforms existing methods on a vision-linguistic compositional structure reasoning dataset. |
REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark (2025.acl-long)
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Navve Wasserman, Roi Pony, Oshri Naparstek, Adi Raz Goldfarb, Eli Schwartz, Udi Barzelay, Leonid Karlinsky
| Challenge: | Existing benchmarks do not fully capture real-world retrieval challenges . existing benchmarks lack a complete understanding of how models perform in realistic setups . |
| Approach: | They propose an automatic benchmark to address four key properties essential for real-world retrieval: (i) multi-modal documents, (ii) enhanced difficulty, ( (iv) Realistic-RAG queries and (v) accurate labeling. |
| Outcome: | The proposed model reveals significant model weaknesses, particularly in handling table-heavy documents and robustness to query rephrasing. |
LoRE: Enhancing Search Relevance with Progressive Chain-of-Thought and Preference Alignment (2026.findings-acl)
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Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, null Lihaoran, Songyan Liu, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng
| Challenge: | E-commerce search relevance is a critical component of retrieval systems. |
| Approach: | They propose a large-generative model for search relevance that trains reasoning knowledge, multi-modal understanding and rule awareness into three core competencies. |
| Outcome: | The proposed model outperforms GPT-5 in Macro-F1 and achieves 27% online gain. |
DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering (2025.acl-long)
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Rong Cheng, Jinyi Liu, Yan Zheng, Fei Ni, Jiazhen Du, Hangyu Mao, Fuzheng Zhang, Bo Wang, Jianye Hao
| Challenge: | Existing approaches to multi-hop question answering struggle to identify and organize dynamic knowledge . et al., 2023; Liu e.t. al. 2023) suggest a dual-process framework for multi-step reasoning . |
| Approach: | They propose a synergistic dual-process framework that integrates reasoning and retrieval. |
| Outcome: | The proposed framework improves answer accuracy and coherence even in smaller-scale models. |
X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning (2025.emnlp-main)
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| Challenge: | Existing text-to-video retrieval systems use embedding models for feature extraction and compute cosine similarities for ranking. |
| Approach: | They propose an explainable retrieval framework upon LLM CoT reasoning to replace embedding models for feature extraction and ranking. |
| Outcome: | The proposed retrieval framework improves retrieval performance and produces detailed rationales. |
HiGMem: A Hierarchical and LLM-Guided Memory System for Long-Term Conversational Agents (2026.findings-acl)
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| Challenge: | Existing memory systems rely on vector similarity for retrieval, resulting in bloated evidence sets . existing systems produce little additional recall, but this approach lowers retrieval precision . |
| Approach: | They propose a two-level event-turn memory system that uses event summaries as semantic anchors to predict which related turns are worth reading. |
| Outcome: | The proposed system achieves the best F1 on four of five question categories and improves adversarial F1 from 0.54 to 0.78 over A-Mem while retrieving an order of magnitude fewer turns. |
Cross-lingual Matryoshka Representation Learning across Speech and Text (2026.findings-acl)
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| Challenge: | Speakers of under-represented languages face language barriers and modality barriers . we train a bilingual speech-text embedding model for French-Wolof . |
| Approach: | They train a bilingual speech-text Matryoshka embedding model that enables efficient retrieval of French text from Wolof speech queries. |
| Outcome: | The proposed model can retrieve French text from Wolof speech queries without expensive ASR-translation pipelines. |
LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning (2026.acl-long)
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Zerui Chen, Qinggang Zhang, Zhishang Xiang, Zhimin Wei, Linfeng Gao, Xiao Huang, Zhihong Zhang, Jinsong Su
| Challenge: | Graph-based Retrieval-Augmented Generation (GraphRAG) is a new approach to document retrieval, but it is not suitable for legal reasoning. |
| Approach: | They propose a framework for reliable legal reasoning that structures knowledge as relational graphs and uses a multi-agent system to verify validity. |
| Outcome: | The proposed framework outperforms existing GraphRAG models in accurate and trustworthy legal analysis. |
Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models (2025.emnlp-main)
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Sina Semnani, Jirayu Burapacheep, Arpandeep Khatua, Thanawan Atchariyachanvanit, Zheng Wang, Monica Lam
| Challenge: | a new study examines the accuracy of Wikipedia's factual inconsistencies . a corpus-level inconsistent detection system can help editors identify inconsistances . |
| Approach: | They propose a corpus-level inconsistency detection system that combines LLM reasoning with retrieval to detect and contextualize potential contradictions for human review. |
| Outcome: | The proposed system can detect inconsistencies in Wikipedia and human review. |
Retrieving Support to Rank Answers in Open-Domain Question Answering (2025.emnlp-main)
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| Challenge: | a novel question answering architecture retrieves content relevant to the combined pair . previous work on automatic claim verification has shown hallucinations . |
| Approach: | They propose a question-answer architecture that prioritizes supporting evidence . it retrieves paragraphs that directly substantiate the correctness of a with respect to q . |
| Outcome: | The proposed approach can be used by large language models to retrieve explanatory paragraphs that ground their reasoning. |
RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine (2026.findings-acl)
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Jiatan Huang, Mingchen Li, Zonghai Yao, Dawei Li, Yuxin Zhang, Zhichao Yang, Yongkang Xiao, Feiyun Ouyang, Xiaohan Li, Shuo Han, Hong yu
| Challenge: | Existing methods for retrieving medical textual knowledge Graphs struggle to perform well, a study finds . existing methods struggle to provide accurate answers to complex questions, he says . |
| Approach: | They synthesize user queries integrating diverse topological structures, relational information, and complex textual descriptions. |
| Outcome: | a new dataset for medical textual knowledge graphs shows that existing methods struggle to perform well . main bottlenecks lie in the scarcity of existing medical TKGs and the limited expressiveness of their topological structures . |
Evaluating Perspectival Biases in Cross-Modal Retrieval (2026.findings-acl)
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Teerapol Saengsukhiran, Peerawat Chomphooyod, Narabodee Rodjananant, Chompakorn Chaksangchaichot, Patawee Prakrankamanant, Witthawin Sripheanpol, Pak Lovichit, Sarana Nutanong, Ekapol Chuangsuwanich
| Challenge: | a recent study shows that multimodal retrieval systems are expected to operate in a semantic space, agnostic to the language or cultural origin of the query. |
| Approach: | They introduce a benchmark to quantify linguistic and cultural biases in multimodal retrieval systems . they propose a framework to decouple language from culture and decouples it from semantics . |
| Outcome: | The proposed benchmark systematically measures the effects of linguistic and cultural biases on retrieval performance. |
MemCoRL: Alternating Co-Optimization of Memory Retrieval and Utilization via Collaborative Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing research has proposed external memory modules for Large Language Models (LLMs) to overcome the limitations of finite input length and obtain contextual memory beyond the current input. |
| Approach: | They propose a two-stage alternating co-optimization reinforcement learning method that optimizes evidence retrieval and utilization using semantic feedback and rewards. |
| Outcome: | The proposed method outperforms baselines on lexical overlap and semantic similarity metrics, confirming the co-optimization in memory retrieval and memory utilization. |
Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning (2026.findings-acl)
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Yue Guo, Fanfu Wang, Jianwei Lv, Xincheng Shi, Yuchen Li, Youya Wang, Yunsheng Zeng, Yujing Liu, Yunhao Qiao, Gen Li, Junfeng Wang, Bo Yuan
| Challenge: | Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face high maintenance costs and low generalization capability. |
| Approach: | They propose a clinical diagnostic model with clinical reasoning and inquiry skills, the Dr. Assistant, and a pipeline to capture abstract reasoning logic. |
| Outcome: | The proposed model outperforms open-source models and achieves competitive performance to closed-source model. |
D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory (2026.findings-acl)
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| Challenge: | Conventional interactive algorithms have predominantly treated memory as a contextual element, neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval. |
| Approach: | They propose a multi-turn dialogue dataset with Personalized Contextual Memory to facilitate advanced research on personalized memory processing. |
| Outcome: | The proposed datasets provide a comprehensive benchmark to facilitate advanced research on personalized memory processing. |
Structure Guided Retrieval-Augmented Generation for Factual Queries (2026.acl-long)
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| Challenge: | Existing methods for RAG produce factually incorrect outputs, resulting in incorrect answers. |
| Approach: | They propose a novel problem that explicitly incorporates structural information into RAG for factual questions to satisfy all query conditions. |
| Outcome: | The proposed method significantly outperforms baselines on ERQA while maintaining reasonable computational overhead. |
LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations (2026.acl-long)
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| Challenge: | a new framework for text embedding models is available for free . asymmetrical architectures allow for flexible asymmetry, while asynchronous architectures require small batches . |
| Approach: | They propose a knowledge distillation framework for text embedding models that is compatible with their teacher . they publish leaf-ir, a 23M parameters information retrieval oriented model that ranks no.1 on BEIR . |
| Outcome: | The proposed model is compatible with teacher, enabling flexible asymmetric architectures . it sets a new state-of-the-art (SOTA) on BEIR, and achieves no.1 on the leaderboard . |
Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding (2026.acl-long)
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Ke Ma, Jiaqi Tang, Bin Guo, Xueting Han, Ruonan Xu, Qingfeng He, Ziheng Wang, Xu Wang, Qifeng Chen, Zhiwen Yu, Yunhao Liu
| Challenge: | Existing methods for streaming video understanding are query-agnostic and implicitly model video evidence. |
| Approach: | They propose a framework that establishes explicit, structured alignment between the accumulated video evidence and the query’s expected response conditions via scene graphs. |
| Outcome: | The proposed model achieves more interpretable and accurate response timing decisions on both proactive and reactive tasks. |
Progressive Re-ranking for Multimodal Retrieval-Augmented Generation via Curriculum Learning (2026.findings-acl)
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| Challenge: | Existing approaches to improve retrieval performance of large language models are limited by static knowledge. |
| Approach: | They propose a multimodal re-ranking framework that combines curriculum learning with fine-grained reranking and multimodal section reassessment to improve CLIP-based visual coarse-grain retrieval. |
| Outcome: | The proposed framework achieves state-of-the-art answer accuracy and competitive retrieval performance on InfoSeek and Enc-VQA. |