arXiv subjects

Keerthana Murugaraj

Publications and source records attributed to Keerthana Murugaraj.

3 recordsLinked to original sources

arXiv2026

RECTIFY: An Interactive Workbench for Post-Evaluation RAG Diagnosis, Repair, and Verification

Retrieval-Augmented Generation (RAG) evaluators can identify failures such as weak retrieval, poor grounding, incomplete answers, and unsupported generation, but they rarely help developers decide what to repair next. We present RECTIFY, an interactive Streamlit workbench that turns evaluated RAG cases into auditable repair workflows. RECTIFY filters cases that do not require repair, routes remaining failures into actionable families and finegrained repair slices, and generates editable repair cards that developers can approve, reject, or verify through sandbox reruns. On a controlled RAG benchmark, RECTIFY surfaces interpretable failure profiles across BM25, dense, and hybrid retrieval: BM25 mainly triggers noisy-retrieval repairs, while dense and hybrid retrieval leave smaller sets of multi-part underretrieval and underused-evidence cases. Additional analyses show that pre-filtering reduces unnecessary repair candidates and that slicelevel routing yields more targeted repair cards than broad family-level diagnosis. RECTIFY is publicly available as an open-source Streamlit workbench 1 for helping developers turn evaluation results into inspectable repair decisions.

cs.SE↗

arXiv2025

Automating Historical Insight Extraction from Large-Scale Newspaper Archives via Neural Topic Modeling

Extracting coherent and human-understandable themes from large collections of unstructured historical newspaper archives presents significant challenges due to topic evolution, Optical Character Recognition (OCR) noise, and the sheer volume of text. Traditional topic-modeling methods, such as Latent Dirichlet Allocation (LDA), often fall short in capturing the complexity and dynamic nature of discourse in historical texts. To address these limitations, we employ BERTopic. This neural topic-modeling approach leverages transformerbased embeddings to extract and classify topics, which, despite its growing popularity, still remains underused in historical research. Our study focuses on articles published between 1955 and 2018, specifically examining discourse on nuclear power and nuclear safety. We analyze various topic distributions across the corpus and trace their temporal evolution to uncover long-term trends and shifts in public discourse. This enables us to more accurately explore patterns in public discourse, including the co-occurrence of themes related to nuclear power and nuclear weapons and their shifts in topic importance over time. Our study demonstrates the scalability and contextual sensitivity of BERTopic as an alternative to traditional approaches, offering richer insights into historical discourses extracted from newspaper archives. These findings contribute to historical, nuclear, and social-science research while reflecting on current limitations and proposing potential directions for future work.

cs.CL↗

arXiv2025

RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation

Evaluating Retrieval-Augmented Generation (RAG) systems remains a challenging task: existing metrics often collapse heterogeneous behaviors into single scores and provide little insight into whether errors arise from retrieval,reasoning, or grounding. In this paper, we introduce RAGVUE, a diagnostic and explainable framework for automated, reference-free evaluation of RAG pipelines. RAGVUE decomposes RAG behavior into retrieval quality, answer relevance and completeness, strict claim-level faithfulness, and judge calibration. Each metric includes a structured explanation, making the evaluation process transparent. Our framework supports both manual metric selection and fully automated agentic evaluation. It also provides a Python API, CLI, and a local Streamlit interface for interactive usage. In comparative experiments, RAGVUE surfaces fine-grained failures that existing tools such as RAGAS often overlook. We showcase the full RAGVUE workflow and illustrate how it can be integrated into research pipelines and practical RAG development. The source code and detailed instructions on usage are publicly available on GitHub

cs.CL↗