activity
20232026
most citedHow (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries

3 citations · 3 across the 13 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CL2024

Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models

Somnath Banerjee, Sayan Layek, Hari Shrawgi +7

As LLMs are increasingly deployed in global applications, the importance of cultural sensitivity becomes paramount, ensuring that users from diverse backgrounds feel respected and…

cs.CL2024

SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models

Somnath Banerjee, Sayan Layek, Soham Tripathy +3

Safety-aligned language models often exhibit fragile and imbalanced safety mechanisms, increasing the likelihood of generating unsafe content. In addition, incorporating new knowle…

cs.CL2024

Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations

Rima Hazra, Sayan Layek, Somnath Banerjee +1

Ensuring the safe alignment of large language models (LLMs) with human values is critical as they become integral to applications like translation and question answering. Current a…

cs.CL2024

Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance

Somnath Banerjee, Avik Halder, Rajarshi Mandal +4

The integration of pretrained language models (PLMs) like BERT and GPT has revolutionized NLP, particularly for English, but it has also created linguistic imbalances. This paper s…

cs.CL2024★ 3 cited

How (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries

Somnath Banerjee, Sayan Layek, Rima Hazra +1

In this study, we tackle a growing concern around the safety and ethical use of large language models (LLMs). Despite their potential, these models can be tricked into producing ha…

cs.CL2024

Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models

Rima Hazra, Sayan Layek, Somnath Banerjee +1

In the rapidly advancing field of artificial intelligence, the concept of Red-Teaming or Jailbreaking large language models (LLMs) has emerged as a crucial area of study. This appr…