Aug 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 32 references
TL;DR
This work introduces a trust based adaptive reranking model- ATM (Adaptive Trust Model) that allocates computational resources according to file level uncertainty, instead of assigning a fixed number of reranker calls per query, which focuses computation only where ranking confidence is low.
Abstract
A fundamental change in information retrieving (IR) has been brought about by the quick development of large language patterns (LLMs), which go beyond standard keyword inquiries and ranked outcome lists. Retrieving-Augmented Generation that followed, a more interactive and lively regaining process that incorporates different facets of Accessibility to data into the conversation amongst an individual and the internet engines for searching and exploring, is one of the new interaction forms introduced by LLMs, which are now crucial to the development of IR technologies. We examine the complex effects of LLMs on IR, focusing on three different layers from which they have become essential to the retrieving process: the interaction layer, the structure for obtaining information and a computation pipeline functionality that can leverage a richer meaning representation through sophisticated language patterns, as well as the larger IR ecology. This work introduces a trust based adaptive reranking model- ATM (Adaptive Trust Model)that allocates computational resources according to file level uncertainty. Instead of assigning a fixed number of reranker calls per query, ATM focuses computation only where ranking confidence is low. This concentrate on prejudice, fairness, and ethical considerations in addition to evaluation challenges for the latter. The model gives 15–30% reduction in floating point operations (FLOPs) and up to 20% lower latency while maintaining or improving retrieval precision. To illustrate the influence on one area of study, we point to a few current examples of LLMs being employed in the medical field
The emergence of Large Language Models (LLMs) has redefined how users interact with information in digital environments. However, their widespread and often indiscriminate integration has raised significant concerns regarding reliability and trustworthiness issues that are particularly critical when accessing digital libraries and historical archives. How can one leverage the generalization capacity of an LLM without losing the level of accountability required for an archival institution? In this paper, we present an agentic retrieval system designed to deliver more accurate and verifiable access to historical data while preserving much of the flexibility associated with unconstrained LLMs. As a contribution to historical document analysis, we compare traditional Retrieval-Augmented Generation (RAG) with an agentic GraphRAG architecture in their ability to deliver historical information under realistic conditions, including the presence of OCR and transcription errors. We introduce a semi-symbolic framework that integrates word-spotting techniques for post-OCR correction with a knowledge graph representation that enables the agent to access information through synthesized queries. The interleaved collaboration between word spotting and code generation allows the agent to construct strong retrieval queries that are robust to misinterpretation and hallucination, while still leveraging approximate search when noise and uncertainty, common in historical document analysis, would otherwise hinder precise retrieval.
S. Nicolau, Adrià Molina, O. R. Terrades et al.· 0 citations
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
Information access has evolved from search to ask to act. For more than two decades, information retrieval made networked information usable through crawling, indexing, ranking, user modeling, and recommendation. My early work on Web page understanding, vertical search, learning-to-rank, personalized search, and recommendation followed this user-centered view. This keynote briefly revisits that trajectory as the context for a large shift recently: from retrieving documents to constructing systems that reason over evidence, generate grounded answers, and execute information-seeking tasks. I will discuss WenLan, YuLan, and LLaDA as examples of how multimodal foundation models, large language models, and diffusion language models reshape the interface between retrieval and generation. More importantly, I will focus on retrieval-augmented generation and AI search agents. FlashRAG provides a modular open-source platform for RAG research; Search-o1 and WebThinker connect large reasoning models with active search and deep research; and DeepAgent studies scalable tool use for general reasoning. Together, these works expose new IR problems: when and what to retrieve, how to align model and retriever knowledge preferences, how to refine long-context evidence, how to maintain memory and source credibility, and how to evaluate multi-step search behavior. I will close by arguing that future IR systems should not stop at answers. Large-model-driven agents can plan, search, verify, call tools, and support decisions. RAG and AI search agents turn information access into an iterative process of evidence gathering, grounded reasoning, tool use, and responsible action, linking retrieval to decision support and human-centered applications.
Ji-rong Wen· Annual International ACM SIG...· 0 citations
This work proposes MARS, a scalable knowledge graph question answering (KGQA) approach that requires no model fine-tuning, and performs a structured retrieval procedure that links question entities to the KG and iteratively retrieves relevant next-hop information.
Nikit Srivastava, Daniel Vollmers, René Speck et al.· 0 citations
The proliferation of AI-generated content is fundamentally altering the information ecosystems in which retrieval systems operate. Search engines, recommender systems, and retrieval-augmented generation pipelines increasingly function in mixed information environments where synthetic and human-authored content are tightly interwoven, raising system-level challenges for information retrieval. Key issues include limitations in evaluation validity, as traditional metrics designed for human-authored corpora fail to capture the distinctive properties of AI-generated content; shifts in retrieval behavior and ranking dynamics, as systems may inadvertently favor procedurally generated but weakly grounded information; and challenges to user trust, as assumptions about the provenance and reliability of retrieved human content become more difficult to distinguish from generated content. Rather than focusing only on model-centric performance comparisons, this workshop aims to provide a forum to analyze these implications with an emphasis on reflection, evaluation, and human-centered system design, and to foster community-driven discussion that may inform future evaluation efforts, including potential shared tasks or tracks in venues such as TREC, CLEF, FIRE, or NTCIR.
Ping Liu, Zhedong Zheng, Shane Culpepper et al.· Annual International ACM SIG...· 0 citations
Abstract The data science and artificial intelligence, optimizing information retrieval tasks has become crucial for extracting actionable insights from vast amounts of data. The problem is the need for precise query formulation to retrieve relevant data effectively, as LLMs can generate vast amounts of information that might include noise or irrelevant details. The objective of this study is to enhance the efficiency and accuracy of information retrieval tasks by leveraging large language models (LLMs) for data augmentation. Gathering a diverse dataset from various sources like online search engines, social media platforms, and online forums is crucial for meeting text and information needs effectively. The term frequency-inverse document frequency (TF-IDF) technique is applied to calculate the importance of each term in the dataset, allowing for the differentiation of significant words from common ones. This step is crucial in the data pre-processing phase to enhance the relevance and precision of information retrieval tasks. The goal is to optimize the normalized discounted cumulative gain (NDCG) metric, which measures the ranking quality of the retrieved documents. The framework integrates the fine-tuned LLM model with the chosen information retrieval system by incorporating the model’s outputs into ranking algorithms. The results show that the proposed method has the maximum accuracy, with an average accuracy of around 10 % when implemented using Python software. The future scope for optimizing information retrieval tasks with large language models (LLMs) for data enhancement is vast and promising.
C. Vaidya, Amudhavel Jayavel, Pradeep Kumar Mishra et al.· Journal of Nonlinear, Comple...· 0 citations