Papers by Shaona Ghosh
Guardrails and Security for LLMs: Safe, Secure and Controllable Steering of LLM Applications (2025.acl-tutorials)
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Traian Rebedea, Leon Derczynski, Shaona Ghosh, Makesh Narsimhan Sreedhar, Faeze Brahman, Liwei Jiang, Bo Li, Yulia Tsvetkov, Christopher Parisien, Yejin Choi
| Challenge: | Pretrained generative models provide novel ways for users to interact with computers. |
| Approach: | This tutorial provides an overview of key guardrail mechanisms developed for LLMs along with evaluation methodologies and a detailed security assessment protocol. |
| Outcome: | This tutorial provides an overview of key guardrail mechanisms developed for LLMs, along with evaluation methodologies and a detailed security assessment protocol. |
A Simple Yet Effective Method for Non-Refusing Context Relevant Fine-grained Safety Steering in LLMs (2025.emnlp-main)
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| Challenge: | Existing methods for fine-tuning large language models to meet safety policies are costly and impractical. |
| Approach: | They propose a method to fine-tune large language models to meet evolving safety policies by applying a gradient-free, unsupervised approach. |
| Outcome: | The proposed method provides precise control, avoids blanket refusals, and directs models to generate safe, relevant content. |
CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues (2024.findings-emnlp)
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| Challenge: | Recent advances in instruction-tuning datasets focus on specific tasks like mathematical or logical reasoning. |
| Approach: | They propose to use synthetic dialogues to help language models remain focused on the subject at hand during task-oriented interactions. |
| Outcome: | The proposed dataset improves language models' ability to maintain topical coherence compared to general-purpose instruction-tuned LLMs like gpt-4-turbo and Mixtral-Instruct. |
PROBE: PROcess-Based BEnchmark for Hallucination Detection (2026.findings-acl)
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Yu Zhang, Peter Belcak, Shizhe Diao, Yonggan Fu, Shaona Ghosh, Morteza Mardani, Eileen Margaret Peters Long, Bei Yu, Pavlo Molchanov
| Challenge: | Existing agentic applications rely on LLMs to self-assess the factuality of outputs . but current LLM systems fail to detect hallucinations . |
| Approach: | They propose a benchmark that breaks down hallucination detection into four critical steps . they show that when halluciation detection is treated as a multi-step process, all models achieve considerably better performance. |
| Outcome: | The proposed benchmark breaks down hallucination detection into four critical steps . it shows that when halluciation detection is treated as a multi-step process, all models achieve considerably better performance. |
AEGIS2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails (2025.naacl-long)
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Shaona Ghosh, Prasoon Varshney, Makesh Narsimhan Sreedhar, Aishwarya Padmakumar, Traian Rebedea, Jibin Rajan Varghese, Christopher Parisien
| Challenge: | Existing safety-related content safety models are not well-suited for commercial use. |
| Approach: | They propose a taxonomy that can be used to categorize safety risks . it combines human annotations with a multi-LLM "jury" system to assess safety . they plan to open-source Aegis2.0 data and models to aid in safety guardrailing . |
| Outcome: | The proposed taxonomy can be used to assess the safety of human-LLM interactions . it can be trained on large, non-commercial datasets and is open-source . |