Leveraging Language Models for Textual Retrieval in the TREC 2025 DRAGUN Track

Conference Jul 2026

An Intelligent PDF Question-Answering System; A Retrieval-Augmented Generation Approach

Due to the surge in digital document creation, intelligent systems that can effectively retrieve accurate information from unstructured PDF content have become a necessity. Current keyword-based search techniques lack semantic meaning, while large language models (LLMs), when utilized individually, produce hallucinated...

Mukesh Lakshmi Sai Medikonda, Kalva Vishnu Teja, K. Greeshma et al. · International Conference Com... · 0 citations

Preprint Aug 2026

CeQe: Grounding Lexical Retrieval in Semantic Evidence

This work presents Cross-Encoder Query Expansion (CE-QE), which reads the per-token relevance attributions of a cross-encoder applied to top semantic search results, selects the terms the cross-encoder treats as decisive, and appends them to the BM25 query.

Adam Kahirov, Umesh Deshpande, S. Sundararaman · 0 citations

Conference Jul 2026

Conversational Query Reformulation Using Fine-Grained Retrieval and Keyword Augmentation

Conversational Query Reformulation (CQR) is an important component in Conversational Question Answering (ConvQA), where user queries are often incomplete, ambiguous, and dependent on previous dialogue turns. Recent CQR approaches have shown the effectiveness of large language models (LLMs) in generating standalone quer...

Andhika Putra Bagaskara, Arie Ardiyanti Suryani · International Conference on... · 0 citations

Review Open access Jul 2026

Retrieval-Augmented Generation for Trustworthy Enterprise LLM Assistants

This paper presents a technical synthesis of RAG for trustworthy enterprise assistants and describes the end-to-end pipeline in full: document chunking, embedding, vector indexing, retrieval, cross-encoder re-ranking, and grounded generation with inline citations.

Bini P. B. · International Journal of Inf... · 0 citations

CITADEL — Citation-Driven Draft-Evaluate Loop

This approach combines classical retrieval methods with the usage of multiple large language model (LLM) agents to generate concise, evidence-based reports to align with DRAGUN’s goal of supporting critical engagement with news.

Daniel Seredensky, D. Iddings, S. Small · 0 citations

Open access Jul 2026

Optimized Hybrid Retrieval-Augmented Generation Framework using Semantic Search and Re-ranking for Reliable LLM Responses

Evaluation on a multi-page technical PDF document shows that the hybrid retrieval and re-ranking stages together raise retrieval precision and reduce irrelevant or unsupported answers compared with retrieval limited to a single method, supporting the use of this approach for reliable, document-grounded question answeri...

Vishwa K Dave, K. Pallavi · International Research Journ... · 0 citations