6 citations · 22 across the 27 of their papers we have counts for
28 papers
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…
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…
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…
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…
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…
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks
Somnath Banerjee, Maulindu Sarkar, Punyajoy Saha +2
Recently, influence functions present an apparatus for achieving explainability for deep neural models by quantifying the perturbation of individual train instances that might impa…
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…
Redefining Developer Assistance: Through Large Language Models in Software Ecosystem
Somnath Banerjee, Avik Dutta, Sayan Layek +3
In this paper, we delve into the advancement of domain-specific Large Language Models (LLMs) with a focus on their application in software development. We introduce DevAssistLlama,…
Evaluating the Ebb and Flow: An In-depth Analysis of Question-Answering Trends across Diverse Platforms
Rima Hazra, Agnik Saha, Somnath Banerjee +1
Community Question Answering (CQA) platforms steadily gain popularity as they provide users with fast responses to their queries. The swiftness of these responses is contingent on…
Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities
Rima Hazra, Debanjan Saha, Amruit Sahoo +2
Community Question Answering (CQA) in different domains is growing at a large scale because of the availability of several platforms and huge shareable information among users. Wit…
Hate Speech and Offensive Language Detection in Bengali
Mithun Das, Somnath Banerjee, Punyajoy Saha +1
Social media often serves as a breeding ground for various hateful and offensive content. Identifying such content on social media is crucial due to its impact on the race, gender,…