Papers by Christopher Parisien
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. |
NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails (2023.emnlp-demo)
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| Challenge: | NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems. |
| Approach: | They propose to add programmable guardrails to LLMs that are user-defined, independent of the underlying LLM, and interpretable. |
| Outcome: | The proposed approach can be used with several LLM providers to develop controllable and safe LLM applications using programmable rails. |
Unsupervised Extraction of Dialogue Policies from Conversations (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are used to extract dialogue policies from conversational data. |
| Approach: | They propose a method for extracting dialogue policies from conversational data using canonical forms and graph traversal algorithms. |
| Outcome: | The proposed method gives conversation designers greater control and improves the process of developing dialogue policies. |
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. |
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 . |
Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models (2025.findings-emnlp)
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| Challenge: | Recent research shows that reasoning-based language models offer significant benefits for LLM safety and guardrail applications. |
| Approach: | They conduct an analysis of reasoning-based guardrail models for content moderation . they find reasoning models exhibit strong sample efficiency and inference efficiency . |
| Outcome: | The reasoning-based guardrail models show strong performance across domains . the models achieve competitive performance with significantly fewer training examples . |