arXiv subjects

SaiBarath Sundar

Publications and source records attributed to SaiBarath Sundar.

3 recordsLinked to original sources

arXiv2026

Domain Agnostic Text Redaction from Natural Language Rules using Instruction Tuning

With the increasing digitization of personal and corporate communication, the automatic sanitization of textual data has become a crucial component of data privacy and compliance frameworks. Traditional text sanitization solutions are majorly suitable for obscuring sensitive data with standard structure such as Personal Identifiable Information (PII). These solutions do not provide transparent justification for their redaction, which makes it difficult to audit them. This paper introduces an explainable, domain-agnostic text redaction solution that uses natural language rules of redaction, applied via an instruction-tuned language model, to identify and redact sensitive information in unstructured documents. Unlike traditional text sanitization, this method enables a user to conveniently define any sensitive information; which may be structured (e.g.\ PII) or unstructured (e.g.\ legal terms and conditions) in natural language. A general-purpose LLM generates or augments these natural language rules of redaction from the user's definition, which are then used to instruction-fine-tune a smaller language model that reasons the rules step-by-step over any given document to identify and redact the corresponding sensitive content, while providing transparent justifications for each redaction and highlighting the specific rule that triggered the decision. This explanation is generated in natural language to support human reviewers and auditors in understanding why specific content was redacted. A reconstruction-based metric is used to estimate the probability of recovering redacted information from the sanitized document, quantifying redaction coverage. The solution shows high reconstruction error and high redaction precision, making it suitable for automated text sanitization in critical applications such as legal discovery, medical documentation, and corporate information governance.

cs.CL↗

arXiv2025

Small sample-based adaptive text classification through iterative and contrastive description refinement

Zero-shot text classification remains a difficult task in domains with evolving knowledge and ambiguous category boundaries, such as ticketing systems. Large language models (LLMs) often struggle to generalize in these scenarios due to limited topic separability, while few-shot methods are constrained by insufficient data diversity. We propose a classification framework that combines iterative topic refinement, contrastive prompting, and active learning. Starting with a small set of labeled samples, the model generates initial topic labels. Misclassified or ambiguous samples are then used in an iterative contrastive prompting process to refine category distinctions by explicitly teaching the model to differentiate between closely related classes. The framework features a human-in-the-loop component, allowing users to introduce or revise category definitions in natural language. This enables seamless integration of new, unseen categories without retraining, making the system well-suited for real-world, dynamic environments. The evaluations on AGNews and DBpedia demonstrate strong performance: 91% accuracy on AGNews (3 seen, 1 unseen class) and 84% on DBpedia (8 seen, 1 unseen), with minimal accuracy shift after introducing unseen classes (82% and 87%, respectively). The results highlight the effectiveness of prompt-based semantic reasoning for fine-grained classification with limited supervision.

cs.LG↗

arXiv2025

I2I-STRADA -- Information to Insights via Structured Reasoning Agent for Data Analysis

Recent advances in agentic systems for data analysis have emphasized automation of insight generation through multi-agent frameworks, and orchestration layers. While these systems effectively manage tasks like query translation, data transformation, and visualization, they often overlook the structured reasoning process underlying analytical thinking. Reasoning large language models (LLMs) used for multi-step problem solving are trained as general-purpose problem solvers. As a result, their reasoning or thinking steps do not adhere to fixed processes for specific tasks. Real-world data analysis requires a consistent cognitive workflow: interpreting vague goals, grounding them in contextual knowledge, constructing abstract plans, and adapting execution based on intermediate outcomes. We introduce I2I-STRADA (Information-to-Insight via Structured Reasoning Agent for Data Analysis), an agentic architecture designed to formalize this reasoning process. I2I-STRADA focuses on modeling how analysis unfolds via modular sub-tasks that reflect the cognitive steps of analytical reasoning. Evaluations on the DABstep and DABench benchmarks show that I2I-STRADA outperforms prior systems in planning coherence and insight alignment, highlighting the importance of structured cognitive workflows in agent design for data analysis.

cs.AI↗