Papers with retrieval and
From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems (2025.acl-srw)
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| Challenge: | Retrieval-augmented generation (RAG) is a key framework in natural language processing . however, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents . |
| Approach: | They investigate how entity coreference affects document retrieval and generative performance in RAG-based systems. |
| Outcome: | The proposed model improves QA performance and retrieval relevance and contextual understanding. |
Chain-of-Rewrite: Aligning Question and Documents for Open-Domain Question Answering (2024.findings-emnlp)
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Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing approaches to answer open-domain question have encountered term mismatch and limited interaction between IR systems and large language models. |
| Approach: | They propose a method which leverages the guidance and feedback gained from the analysis to provide faithful and consistent extensions for effective question answering. |
| Outcome: | Experiments on four open-domain question answering datasets show the proposed method performs well under zero-shot settings. |
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval (2022.findings-naacl)
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| Challenge: | Existing approaches to retrieve hard negative sentences are limited in the scale of the dataset thus fail to identify negative samples of high difficulty for every image. |
| Approach: | They propose to use a model to generate synthetic negative sentences with higher difficulty by masking and refilling the images and performing word discrimination and word correction tasks to improve retrieval and generation. |
| Outcome: | The proposed model generates synthetic negative sentences with higher difficulty on MS-COCO and Flickr30K and is robust and faithful to state-of-the-art training. |
External Knowledge Acquisition for End-to-End Document-Oriented Dialog Systems (2023.eacl-main)
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| Challenge: | End-to-end neural models for conversational AI often assume that a response can be generated by considering only the knowledge acquired during training. |
| Approach: | They propose an architecture for document-oriented conversations with access to external knowledge sources. |
| Outcome: | The proposed architecture outperforms baseline models on the Wizard of Wikipedia dataset by 10.3% and 7.4%. |
A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation (2020.acl-main)
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| Challenge: | Recent work shows successful methods for unsupervised machine translation (UMT) initialization stage is important since bad initialization may wrongly squeeze the search space and too much noise may hurt the final performance. |
| Approach: | They propose a retrieval and rewriting based method to better initialize unsupervised translation models. |
| Outcome: | The proposed method improves translation performance by over 4 BLEU scores. |
MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering (2022.findings-emnlp)
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| Challenge: | Conventional HQA models retrieve coarse- or fine-grained evidence to reason the answer . however, they neglect a more general scenario requiring reasoning over heterogeneous data to answer a question. |
| Approach: | They propose a multi-granularity evidence retrieval and reasoning approach to answer questions over heterogeneous data using tables and passages linked to table cells. |
| Outcome: | The proposed approach significantly boosts the performance on the HybridQA dataset. |
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2025.coling-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) models address fairness concerns with respect to sensitive attributes such as gender, geographic location, and other demographic factors. |
| Approach: | They propose a framework to evaluate fairness in RAG using scenario-based questions and analyzing disparities across demographic attributes. |
| Outcome: | The proposed framework analyzes disparities across demographic attributes and identifies fairness issues in retrieval and generation stages. |
MultiConIR: Towards Multi-Condition Information Retrieval (2025.findings-emnlp)
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Xuan Lu, Sifan Liu, Bochao Yin, Yongqi Li, Xinghao Chen, Hui Su, Yaohui Jin, Wenjun Zeng, Xiaoyu Shen
| Challenge: | MultiConIR is a benchmark designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios. |
| Approach: | They propose a benchmark to evaluate retrieval and reranking models under nuanced multi-condition query scenarios. |
| Outcome: | The proposed benchmark evaluates retrieval and reranking models under nuanced multi-condition query scenarios across five domains. |
HyQE: Ranking Contexts with Hypothetical Query Embeddings (2024.findings-emnlp)
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| Challenge: | Existing approaches to rank contexts rely on similarity between contexts and queries, but these methods are limited by the number of candidate contexts. |
| Approach: | They propose a scalable ranking framework that combines embedding similarity and large language models without fine-tuning. |
| Outcome: | The proposed framework improves the performance across multiple benchmarks. |
TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text (2025.findings-acl)
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| Challenge: | Existing methods for identifying adversarial techniques in security texts face a trade-off: generic models with limited domain precision or resource-intensive pipelines. |
| Approach: | They propose a domain-specific retrieval-augmented generation framework that integrates off-the-shelf retrievers, instruction-tuned LLMs, and minimal text–technique pairs. |
| Outcome: | The proposed framework improves retrieval quality and domain specificity without extensive optimizations. |
Dialogue-RAG: Enhancing Retrieval for LLMs via Node-Linking Utterance Rewriting (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) methods have demonstrated significant potential on tasks across multiple domains. |
| Approach: | They propose a lightweight IUR model for query rewriting to complete key information in dialogue to enhance retrieval. |
| Outcome: | The proposed model improves retrieval and generation ability of RAG system in multi-round dialogue scenarios. |
Structured Dialogue Refinement: Building Retrieval-Augmented Question Answering on Goal-Oriented Dialogues (2026.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) is widely used for knowledgeintensive question answering (QA), but a large amount of real-world problem-solving knowledge is captured in goal-oriented dialogues. |
| Approach: | They propose a framework that adapts dialogue corpora for RAG at both retrieval and generation stages without altering the underlying pipeline. |
| Outcome: | The proposed framework improves retrieval quality and QA performance under dialogue-specific structural challenges. |