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 results when they are not contextually aware of document content. In this paper, we introduce a novel real-time question answering system for PDF content using a lightweight approach to Retrieval-Augmented Generation (RAG). Our system combines a HuggingFace sentence transformer model for semantic embeddings with a FAISS vector similarity search for efficient retrieval, leveraging a Groq-hosted LLaMA 3.3 70B model for context-grounded answer generation. The system is built using LangChain for orchestration and Streamlit for web-based deployment, and requires no GPU, no fine-tuning, and no pre-indexed corpus. Evaluation across five complementary metrics—Exact Match, Token-level F1, BLEU, ROUGE, and semantic similarity—demonstrates strong alignment between generated and reference answers, with a peak semantic similarity of 0.8709 and a mean Token F1 of 0.4701, confirming the viability of this approach for real-world document intelligence.

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 queries. However, rewritten queries do not always lead to optimal retrieval performance. This paper presents a retrieval-aware CQR framework that integrates fine-grained retrieval feedback and keyword augmentation to improve downstream document retrieval. Instead of treating the LLM-rewritten query as the final search query, our framework uses it to retrieve candidate documents, identify relevant paragraph-level evidence, generate a pseudo-answer, and extract semantically relevant keywords. These keywords are then filtered based on their alignment with the reformulated query and dialogue history before being incorporated into the final query. We evaluate the proposed framework on a sampled subset of the QReCC dataset using BM25 and GTR as sparse and dense retrieval backbones. The experimental results show that our proposed approach improves retrieval accuracy compared to the baseline model across all metrics such as Recall@10, MRR and NDCG. Specifically, under the BM25 sparse retrieval framework, our best-performing configuration achieves improvements in Recall@10 (81.29), MRR (56.06) and NDCG (60.92) compared to the baseline. A comparable pattern of substantial gains holds under the GTR dense retrieval framework, achieving a Recall@10 (76.51), MRR (53.26) and NDCG (57.54), indicating the effectiveness of our method across different retrieval paradigms.

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 answering.

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