3 citations · 3 across the 13 of their papers we have counts for
11 papers · 1 filter
Structurally-bounded Agentic Graph Exploration for Evidence-Grounded Scholarly DeepSearch
Rima Hazra, Sayan Layek, Somnath Banerjee +2
We present Crase, a bounded and inspectable alternative to deep research agents for scholarly search. Instead of an open-ended search loop, Crase queries a search engine once for s…
SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems
Rima Hazra, Bikram Ghuku, Ilona Marchenko +5
Large language models are rapidly being deployed as AI tutors, yet current evaluation paradigms assess problem-solving accuracy and generic safety in isolation, failing to capture…
ProSocialAlign: Preference Conditioned Test Time Alignment in Language Models
Somnath Banerjee, Sayan Layek, Sayantan Adak +3
Current language model safety paradigms often fall short in emotionally charged or high-stakes settings, where refusal-only approaches may alienate users and naive compliance can a…
Attributional Safety Failures in Large Language Models under Code-Mixed Perturbations
Somnath Banerjee, Pratyush Chatterjee, Shanu Kumar +4
While LLMs appear robustly safety-aligned in English, we uncover a catastrophic, overlooked weakness: attributional collapse under code-mixed perturbations. Our systematic evaluati…
Soteria: Language-Specific Functional Parameter Steering for Multilingual Safety Alignment
Somnath Banerjee, Sayan Layek, Pratyush Chatterjee +2
Ensuring consistent safety across multiple languages remains a significant challenge for large language models (LLMs). We introduce Soteria, a lightweight yet powerful strategy tha…
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…
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…
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…
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…
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…
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…