6 citations · 12 across the 16 of their papers we have counts for
9 papers · 1 filter
Structure-Aware Chunking for Tabular Data in Retrieval-Augmented Generation
Pooja Guttal, Varun Magotra, Vasudeva Mahavishnu +3
Tabular documents such as CSV and Excel files are widely used in enterprise data pipelines, yet existing chunking strategies for retrieval-augmented generation (RAG) are primarily…
From Concept-Aligned Tokens to Vulnerable Features: Mechanistic Localization of Jailbreaks
Nilanjana Das, Mathew Dawit, Aman Chadha +1
Jailbreak attacks expose a persistent failure mode in safety-aligned LLMs: models can be pushed into harmful behavior, but the internal representations enabling this shift remain p…
Generation-Time vs. Post-hoc Citation: A Holistic Evaluation of LLM Attribution
Yash Saxena, Raviteja Bommireddy, Ankur Padia +1
Trustworthy Large Language Models (LLMs) must cite human-verifiable sources in high-stakes domains such as healthcare, law, academia, and finance, where even small errors can have…
Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions
Seyedali Mohammadi, Bhaskara Hanuma Vedula, Hemank Lamba +4
Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across mu…
Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains
Yash Saxena, Ankur Padia, Mandar S Chaudhary +3
Retrieval-Augmented Generation (RAG) systems deployed in sensitive domains must provide interpretable evidence selection and robust safeguards against data poisoning, yet current a…
From Guessing to Asking: An Approach to Resolving the Persona Knowledge Gap in LLMs during Multi-Turn Conversations
Sarvesh Baskar, Tanmay Tulsidas Verelakar, Srinivasan Parthasarathy +1
In multi-turn dialogues, large language models (LLM) face a critical challenge of ensuring coherence while adapting to user-specific information. This study introduces the persona…
Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context
Nilanjana Das, Edward Raff, Aman Chadha +1
As the AI systems become deeply embedded in social media platforms, we've uncovered a concerning security vulnerability that goes beyond traditional adversarial attacks. It becomes…
Towards Robust Evaluation of Unlearning in LLMs via Data Transformations
Abhinav Joshi, Shaswati Saha, Divyaksh Shukla +4
Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vas…
Human-Interpretable Adversarial Prompt Attack on Large Language Models with Situational Context
Nilanjana Das, Edward Raff, Manas Gaur
Previous research on testing the vulnerabilities in Large Language Models (LLMs) using adversarial attacks has primarily focused on nonsensical prompt injections, which are easily…