Papers with retrieval-augmented framework
RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity (2025.findings-naacl)
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| Challenge: | Current approaches to legal summarization struggle with content theme deviation and inconsistent writing styles due to the content of the source document. |
| Approach: | They propose a retrieval-augmented framework that utilizes exemplar summaries along with the source document to guide the model. |
| Outcome: | The proposed model outperforms models that do not utilize exemplars and those that rely on similarity-based exemplar selection. |
Knowledge-augmented Financial Market Analysis and Report Generation (2024.emnlp-industry)
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| Challenge: | Existing methods to generate financial market analysis text require extensive financial knowledge and skill of financial analysts. |
| Approach: | They propose a task to generate financial market analysis reports using financial market data using a financial knowledge graph. |
| Outcome: | The proposed framework outperforms large-scale language models and retrieval-augmented baselines in the financial market analysis generation task. |
Decoding the Market’s Pulse: Context-Enriched Agentic Retrieval Augmented Generation for Predicting Post-Earnings Price Shocks (2026.eacl-long)
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| Challenge: | Existing methods for forecasting large stock price movements after corporate earnings calls are prone to **narrative bias** Existing approaches lack temporal-causal reasoning and are unable to predict large stock prices. |
| Approach: | They propose a retrieval-augmented framework that deploys a team of cooperative LLM agents . they retrieve structured evidence from a Causal-Temporal Knowledge Graph built from financial statements and earnings calls . |
| Outcome: | The proposed framework outperforms larger LLMs and fine-tuned models in macro-F1, MCC, and Sharpe for the same forecasting horizon. |
SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations (2026.findings-eacl)
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| Challenge: | Standard NLP benchmarks often miss subtle, culturally-specific cues in social media . incorporating structured cultural knowledge into the retrieval process improves accuracy by up to 31% . |
| Approach: | They propose a retrieval-augmented framework that integrates a culture-specific slang knowledge graph into large language models via one-shot prompting. |
| Outcome: | The proposed framework outperforms traditional and unstructured retrieval methods in slang-based models by 31% and 28%. |
PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are too large to be fine-tuned with budget constraints and some are only accessible via APIs. |
| Approach: | They propose a pluggable Reward-Driven Contextual Adapter that integrates large language models as generators and trains them to refine the retrieved information. |
| Outcome: | The proposed method improves ReQA performance on three datasets by up to 20% compared to existing methods. |
SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design (2025.emnlp-main)
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Wenxin Tang, Jingyu Xiao, Wenxuan Jiang, Xi Xiao, Yuhang Wang, Xuxin Tang, Qing Li, Yuehe Ma, Junliang Liu, Shisong Tang, Michael R. Lyu
| Challenge: | Existing natural language-based LLM generation methods struggle to capture visual and structural nuances of slide designs. |
| Approach: | They propose a layout-aware framework for generating editable slides from reference images . they propose python code that translates NL instructions into Python code to construct each slide . |
| Outcome: | The proposed framework outperforms state-of-the-art models by up to 40.5 points . it also outperformed open-source models with improved reverse-engineered data. |
Improving Autoformalization Using Direct Dependency Retrieval (2026.acl-long)
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| Challenge: | Existing methods for hallucinate formal dependencies lack scalability and precision to leverage ever-growing public datasets. |
| Approach: | They propose a retrieval-augmented framework based on Direct Dependency Retrieval to generate formal dependencies from natural-language mathematical descriptions and verify their existence via an efficient Suffix Array Check (SAC). |
| Outcome: | The proposed framework outperforms state-of-the-art methods in retrieval precision and recall and can be used to validate formal representations in a public dataset. |
Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have produced significant advances in the field of recommender systems. |
| Approach: | They propose to retrieve up-to-date structure information from the knowledge graph to augment recommendations by leveraging external knowledge sources. |
| Outcome: | Experiments on a large dataset show that the proposed method is effective in enhancing LLM-based recommendations. |