CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to extend context length of Large Language Models (LLMs) still struggle with retrieval and reasoning in long context inputs. |
| Approach: | They propose a coarse-to-fine method to enhance multi-document question-answering capacities by removing background and distracting documents. |
| Outcome: | Experiments show that CAFE outperforms baseline methods on multiple documents. |
Similar Papers
RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) integrates knowledge from tables with an external knowledge base to improve the answer relevance and accuracy. |
| Approach: | They propose a table-corpora-aware RAG framework called T-RAG to integrate external knowledge into Large Language Models (LLMs) they then develop a multi-table question answering benchmark called MultiTableQA which spans 3 different task types, 57,193 tables, and 23,758 questions in total. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy, recall, and runtime performance, with improvements of up to 9.4%. |
Fine-grained Knowledge Enhancement for Retrieval-Augmented Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing studies rely on semantic similarity to retrieve knowledge but ignore fine-grained information within documents. |
| Approach: | They propose a fine-grained knowledge enhancement method to fill knowledge gaps with retrieved external information by a Chain-of-Thought prompting procedure and a decoding enhancement strategy to constrain the document-based decoding process. |
| Outcome: | The proposed method can be applied in a plug-and-play manner to enhance its performance with no additional modules or training process. |
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
| Approach: | They propose a retrieval-augmented large language model that can dynamically select the most suitable strategy based on query complexity. |
| Outcome: | The proposed approach improves the performance of QA systems on open-domain QA datasets. |
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Experimental evaluations on NQ, TriviaQA, and HotpotQA datasets demonstrate that our approach achieves a 90% reduction in retrieval time compared to conventional methods while maintaining considerate recall performance. |
| Approach: | They propose a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. |
| Outcome: | The proposed framework outperforms retrieval/non-retrieval baselines by 1.4-4.3% in EM scores on NQ, TriviaQA, and HotpotQA datasets. |
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression (2026.acl-long)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) extends large language models with external knowledge, but it must balance limited effective context, redundant retrieved evidence, and the loss of fine-grained facts. |
| Approach: | They propose a hybrid RAG framework that uses natural-language snippets and semantic compression vectors to preserve passages in text form and compress remaining evidence into interpretable vectors for iterative evidence reranking. |
| Outcome: | The proposed framework improves answer relevance, answer correctness and semantic similarity across 9 datasets and 5 open-source LLMs. |
Stronger Baselines for Retrieval-Augmented Generation with Long-Context Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing long-context language models (LMs) can handle tens of thousands of tokens in a single context window. |
| Approach: | They compare two recent multi-stage pipelines, ReadAgent and RAPTOR, against three baselines. |
| Outcome: | The proposed pipelines outperform more complex methods on multiple long-context QA benchmarks. |
Open-RAG: Enhanced Retrieval Augmented Reasoning with Open-Source Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to integrate Large Language Models with external knowledge suffer from limited reasoning capabilities, especially when using open-source LLMs. |
| Approach: | They propose a framework that transforms an arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE) model capable of handling complex reasoning tasks. |
| Outcome: | The proposed framework transforms an arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE) model capable of handling complex reasoning tasks, including both single- and multi-hop queries. |
S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA (2026.acl-long)
Copied to clipboard
| Challenge: | Retrieval-augmented generation grounds language models in external evidence, but multi-hop question answering remains difficult . iterative pipelines must control what to retrieve next and when evidence is adequate. |
| Approach: | They propose an iterative framework with an explicit controller, S2G-Judge . they map structured gap items into the next retrieval query to produce stable retrieval trajectories . |
| Outcome: | Experiments on TriviaQA, HotpotQA, and 2WikiMultiHopQA show that S2G-RAG improves multi-hop QA performance and robustness under multi-turn retrieval. |
Coarse-to-Fine Query Focused Multi-Document Summarization (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on query focused multi-document summarization relies heavily on retrieval-style methods. |
| Approach: | They propose a query-cluster-based model which uses more accurate modules for estimating whether text segments are relevant, likely to contain an answer, and central. |
| Outcome: | The proposed framework outperforms strong comparison systems on benchmark datasets across domains and query types. |
MS-RAG: Simple and Effective Multi-Semantic Retrieval-Augmented Generation (2025.emnlp-main)
Copied to clipboard
| 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 . |