Database-Augmented Query Representation for Information Retrieval (2025.emnlp-main)
Copied to clipboard
| 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. |
Similar Papers
Deep Relevance Ranking Using Enhanced Document-Query Interactions (D18-1)
Copied to clipboard
| Challenge: | Document relevance ranking is the task of ranking documents from a large collection using the query and the text of each document only. |
| Approach: | They propose to use convolutional n-gram matching to inject rich context-sensitive encodings into their models, inspired by PACRR's convolution-based ngram matching features. |
| Outcome: | The proposed models outperform baselines, DRMM, and PACRR on the BIOASQ and TREC ROBUST questions and document inputs. |
Reimagining Retrieval Augmented Language Models for Answering Queries (2023.findings-acl)
Copied to clipboard
Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez, Richard James, Xi Victoria Lin, Alon Halevy, Wen-tau Yih
| Challenge: | Large language models (LLMs) are expensive to train, deploy, and maintain, both financially and in terms of environmental impact. |
| Approach: | They present a reality check on large language models and compare their predictions to retrieval-augmented language models. |
| Outcome: | The proposed models fare better on question answering tasks and have become the foundation of impressive demos like Chat-GPT. |
Thesis Proposal: On the Granularity-Robustness Trade-off in Text-Derived Knowledge Graphs (2026.acl-srw)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) based on dense embeddings is a dominant paradigm for text retrieval, but many real-world applications require attribute-specific querying. |
| Approach: | They propose a query-driven framework for constructing and retrieving knowledge graphs from text using dense embeddings. |
| Outcome: | The proposed framework combines the robustness of dense retrieval with the explicit queryability of symbolic representations. |
Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation (2022.acl-short)
Copied to clipboard
| Challenge: | Existing sparse retrieval models rely on term-based matching to retrieve relevant documents. |
| Approach: | They propose a framework which augments the representations of documents with interpolation and perturbation. |
| Outcome: | The proposed framework significantly outperforms baselines on the dense retrieval of both the labeled and unlabeled documents. |
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
Copied to clipboard
Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
ReFSQL: A Retrieval-Augmentation Framework for Text-to-SQL Generation (2023.findings-emnlp)
Copied to clipboard
Kun Zhang, Xiexiong Lin, Yuanzhuo Wang, Xin Zhang, Fei Sun, Cen Jianhe, Hexiang Tan, Xuhui Jiang, Huawei Shen
| Challenge: | Existing methods that align natural language with SQL Language underestimate inherent structural characteristics of SQL and lead to structure errors. |
| Approach: | They propose a retrieval-argument framework that aligns natural language with SQL Language and trains one encoder-decoder-based model to fit all questions. |
| Outcome: | The proposed framework improves accuracy and robustness of text-to-SQL generation on five datasets. |
Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
Copied to clipboard
| Challenge: | Retrieval-augmented generation systems have shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. |
| Approach: | They propose to integrate query expansion, various novel retrieval strategies, and a Contrastive In-Context Learning RAG to improve response quality. |
| Outcome: | The proposed RAGs incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. |
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion (2025.findings-emnlp)
Copied to clipboard
Yujian Liu, Jiabao Ji, Tong Yu, Ryan A. Rossi, Sungchul Kim, Handong Zhao, Ritwik Sinha, Yang Zhang, Shiyu Chang
| Challenge: | Existing methods to integrate external information into a given table neglect the structured nature of the table. |
| Approach: | They propose a simple yet effective method to integrate external information into a given table by first building an augmenting table and then generating a SQL query over the two tables to answer the question. |
| Outcome: | The proposed method outperforms strong baselines on three table QA benchmarks. |
BERT-QE: Contextualized Query Expansion for Document Re-ranking (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to expand query use pseudo relevance feedback (PRF) but they are under-equipped to evaluate the relevance of information pieces used for expansion. |
| Approach: | They propose a query expansion model that leverages the BERT model to select relevant document chunks for expansion. |
| Outcome: | The proposed model significantly outperforms existing models on the TREC Robust04 and GOV2 test collections. |
Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for text-to-SQL semantic parsing are limited to retrieving schemata based on a single query. |
| Approach: | They propose a text-to-sql semantic parser that uses abstract syntax trees to select few-shot examples for retrieval-augmented generation. |
| Outcome: | The proposed model can generate approximated versions of SQL queries in parallel, and shows improvements over state-of-the-art benchmarks. |