3 citations · 5 across the 16 of their papers we have counts for
16 papers
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…
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…
From Fluent to Verifiable: Claim-Level Auditability for Deep Research Agents
Razeen A Rasheed, Somnath Banerjee, Animesh Mukherjee +1
A deep research agent produces a fluent scientific report in minutes; a careful reader then tries to verify the main claims and discovers the real cost is not reading, but tracing:…
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…
MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation
Sayantan Adak, Somnath Banerjee, Rajarshi Mandal +4
Online memes are a powerful yet challenging medium for content moderation, often masking harmful intent behind humor, irony, or cultural symbolism. Conventional moderation systems…
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…
Investigating Nudges toward Related Sellers on E-commerce Marketplaces: A Case Study on Amazon
Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh +2
E-commerce marketplaces provide business opportunities to millions of sellers worldwide. Some of these sellers have special relationships with the marketplace by virtue of using th…
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…
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…
Antitrust, Amazon, and Algorithmic Auditing
Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh +4
In digital markets, antitrust law and special regulations aim to ensure that markets remain competitive despite the dominating role that digital platforms play today in everyone'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…