Papers with retrieval effectiveness
QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration (2024.naacl-demo)
Copied to clipboard
| Challenge: | Formulating effective search queries can be a daunting task for users when they lack expertise in a specific domain or are not proficient in the language of the content. |
| Approach: | QueryExplorer is an interactive query generation, reformulation, and retrieval interface with support for Hug-gingFace generation models and PyTerrier’sretrieval pipelines and datasets. |
| Outcome: | QueryExplorer is an interactive query generation, reformulation, and retrieval interface with support for Hug-gingFace generation models and PyTerrier’sretrieval pipelines and datasets, and extensivelogging of human feedback. |
Statistical Foundations of DIME: Risk Estimation for Practical Index Selection (2026.eacl-short)
Copied to clipboard
Giulio D'Erasmo, Cesare Campagnano, Antonio Mallia, Pierpaolo Brutti, Nicola Tonellotto, Fabrizio Silvestri
| Challenge: | High-dimensional dense embeddings are noisy or redundant, causing performance degradation and causing errors. |
| Approach: | They propose a method that scores each dimension by fusing the embeddings into a query-dependent matrix. |
| Outcome: | The proposed method improves retrieval effectiveness and reduces embedding size by an average 50% of across different models and datasets at inference time. |
ROGRAG: A Robustly Optimized GraphRAG Framework (2025.acl-demo)
Copied to clipboard
| Challenge: | Existing pipelines for large language models struggle with specialized or emerging topics which are rarely seen in the training corpus. |
| Approach: | They propose a multi-stage retrieval mechanism that integrates dual-level with logic form retrieval methods to improve retrieval robustness without increasing computational cost. |
| Outcome: | The proposed framework outperforms Qwen2.5-7B-Instruct and outperformed mainstream methods on seedbench and significantly improves the performance of each component. |
Can’t Hide Behind the API: Stealing Black-Box Commercial Embedding Models (2025.findings-naacl)
Copied to clipboard
| 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. |
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) excel in zero-shot document ranking tasks. |
| Approach: | They propose a prompt-based re-ranking method that requires no further training but is only feasible for reranking a handful of candidates due to computational costs. |
| Outcome: | The proposed method can retrieve documents from the entire corpus without training and with a large amount of paired text data. |
ConQRet: A New Benchmark for Fine-Grained Automatic Evaluation of Retrieval Augmented Computational Argumentation (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for evaluating RAArg are costly and lack long, complex arguments and real-world evidence. |
| Approach: | They propose to use multiple fine-grained LLM judges to evaluate RAArg using a new benchmark that features long and complex human-authored arguments on debated topics. |
| Outcome: | The proposed methods provide better and more interpretable assessments than traditional single-score metrics and even previously reported human crowdsourcing. |
Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness (2021.naacl-main)
Copied to clipboard
| Challenge: | Neural keyphrase generation models can output absent keyphrases, which are keyphrase that do not appear in the source text. |
| Approach: | They propose a finer-grained categorization scheme that sheds more light on the impact of absent keyphrases on scientific document retrieval. |
| Outcome: | The proposed model shows that only 20% of the words that make up keyphrases actually serve as document expansion, but this small fraction behind much of the gains observed in retrieval effectiveness. |
IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing dense retrieval methods rely on static embeddings that obscure bidirectional relationship between queries and documents. |
| Approach: | They propose a framework that augments any black-box dense retrievers with dynamic, bidirectional modulation at inference time. |
| Outcome: | a new framework augments any dense retriever with dynamic, bidirectional modulation at inference time. |
Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work reserves the principle dimensions of query and document embeddings for building more efficient retrieval systems. |
| Approach: | They propose to use Conditional Autoencoder to compress high-dimensional embeddings to maintain the same embeddable distribution and better recover ranking features. |
| Outcome: | The proposed algorithm achieves comparable ranking performance with its teacher model and makes the retrieval system more efficient. |
Understanding Differential Search Index for Text Retrieval (2023.findings-acl)
Copied to clipboard
| Challenge: | Differentiable Search Index (DSI) is a new information retrieval framework . however, due to the black-box nature of the end-to-end neural architecture, it remains unclear to what extent it possesses basic indexing and retrieval abilities. |
| Approach: | They propose a multi-task distillation approach to enhance the retrieval quality without altering the structure of the model. |
| Outcome: | The proposed method outperforms baselines on various datasets. |
KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods that address corpus-level context loss focus on query enrichment through structured relation representations. |
| Approach: | They propose a framework for Contextual Query Retrieval that enriches queries with contextual representations derived from a corpus-centric KG. |
| Outcome: | The proposed framework outperforms strong baselines on RAGBench and MultiHop-RAG datasets in terms of retrieval effectiveness. |
Learning to Select: Query-Aware Adaptive Dimension Selection for Dense Retrieval (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for dense retrieval use pseudo-relevance feedback to model dimension importance . however, they learn global transformations shared across queries and do not model dimension-aware dimension importance. |
| Approach: | They propose a Query-Aware Adaptive Dimension Selection framework that learns to predict per-dimension importance directly from query embedding. |
| Outcome: | The proposed framework improves retrieval effectiveness over the full-dimensional and PRF-based models. |
ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for enhancing dense retrieval with query augmentation ignore the alignment between generation and ranking objectives. |
| Approach: | They propose a unified LLM-augmented dense retrieval framework that jointly optimizes both the LLM and the retriever. |
| Outcome: | Experimental results show that ExpandR outperforms strong baselines, achieving more than 5% improvement in retrieval performance. |
DVCQR: Dual-View Conversational Query Rewriting with Stage-wise Reinforcement Learning (2026.acl-long)
Copied to clipboard
| Challenge: | Recent approaches to improve retrieval effectiveness rely on a single rewrite . however, they often suffer from conflicting optimization signals . |
| Approach: | They propose a dual-view CQR framework that generates two complementary rewrites for each query. |
| Outcome: | Experiments show that DVCQR outperforms state-of-the-art methods on most metrics . the proposed framework generates two complementary rewrites for each query . |
Leveraging Cognitive Complexity of Texts for Contextualization in Dense Retrieval (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to estimate semantic similarity of queries and documents rely on token-level information derived from query/document interactions. |
| Approach: | They propose a new DRM that leverages query/document interactions based on full embedding representations generated by a Transformer-based model. |
| Outcome: | The proposed model outperforms fine-tuning techniques on lightweight bi-encoders and traditional late-interaction models. |
Q2EI: Query-to-Entity Inference for Semantic Condensation in Domain-Specific Retrieval (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing generative expansions introduce redundancy or hallucinations that cause semantic drift. |
| Approach: | They propose a query rewriting strategy that reframes rewrite as semantic condensation rather than expansion. |
| Outcome: | The proposed method outperforms baselines on medical and legal benchmarks while reducing token consumption. |