3 citations · 5 across the 24 of their papers we have counts for
17 papers · 1 filter
Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop
Sayantan Adak, Rima Hazra, Koyel Mukherjee +2
Large language models can produce fluent tu- toring dialogue, but whether they actually help students learn remains unclear. We present a process-centered synthesis of LLM tutoring…
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…
Lost in Interpretation: The Plausibility-Faithfulness Trade-off in Cross-Lingual Explanations
Somnath Banerjee, Pranav Jha, Rima Hazra +1
LLMs deployed multilingually are often audited via English explanations for non-English inputs. We evaluate extractive explanations ''where the model identifies input token spans a…
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…
SafeMath: Safe Solutions for Unsafe Math Word Problems
Sagnik Basu, Subhrajit Mitra, Aman Juneja +3
Recent research points toward LLMs being manipulated through adversarial and seemingly benign inputs, resulting in harmful, biased, or policy-violating outputs. In this paper, we s…
Bridging the Multilingual Safety Divide: Efficient, Culturally-Aware Alignment for Global South Languages
Somnath Banerjee, Rima Hazra, Animesh Mukherjee
Large language models (LLMs) are being deployed across the Global South, where everyday use involves low-resource languages, code-mixing, and culturally specific norms. Yet safety…
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…
AURA: Affordance-Understanding and Risk-aware Alignment Technique for Large Language Models
Sayantan Adak, Pratyush Chatterjee, Somnath Banerjee +3
Present day LLMs face the challenge of managing affordance-based safety risks-situations where outputs inadvertently facilitate harmful actions due to overlooked logical implicatio…
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…
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem
Somnath Banerjee, Avik Dutta, Aaditya Agrawal +2
With the AI revolution in place, the trend for building automated systems to support professionals in different domains such as the open source software systems, healthcare systems…
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…