Papers with document retrieval quality
CuriousLLM: Elevating Multi-Document Question Answering with LLM-Enhanced Knowledge Graph Reasoning (2025.naacl-industry)
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| Challenge: | Large Language Models (LLMs) have achieved significant success in open-domain question answering, however, they continue to face challenges such as knowledge cutoffs and hallucinations. |
| Approach: | They propose a new mechanism that integrates a curiosity-driven reasoning mechanism into an LLM agent to generate relevant follow-up questions. |
| Outcome: | The proposed enhancement integrates a curiosity-driven reasoning mechanism into an LLM agent, enabling it to generate relevant follow-up questions, thereby guiding the information retrieval process more efficiently. |
LLM-Based Dialogue Labeling for Multiturn Adaptive RAG (2025.emnlp-industry)
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Zhiyu Chen, Biancen Xie, Sidarth Srinivasan, Manikandarajan Ramanathan, Rajashekar Maragoud, Qun Liu
| Challenge: | Retrieval-Augmented Generation (RAG) models integrate large language models with external knowledge retrieval . however, building multi-turn RAG-based chatbots for real-world customer service requires additional complexities. |
| Approach: | They propose methods to automatically generate labels for adaptive retrieval components using real customer-agent dialogue data. |
| Outcome: | The proposed method generates labels for components using real customer-agent dialogue data. |
Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking (2026.acl-long)
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| Challenge: | identifying relevant documents for Reasoning-intensive queries remains a challenge . large language models have shown strong performance in zero-shot document reranking . |
| Approach: | They propose a reranking algorithm that estimates contextual relevance by aggregating LLMs' relevance judgments across batches. |
| Outcome: | The proposed algorithm improves nDCG@10 over retrieval and reranking baselines by 15% and 6–21% respectively. |