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Animesh Mukherjee

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

AURA: Affordance-Understanding and Risk-aware Alignment Technique for Large Language Models

Sayantan Adak
Pratyush Chatterjee
Somnath Banerjee
Rima Hazra
Somak Aditya
Animesh Mukherjee

Present day LLMs face the challenge of managing affordance-based safety risks—situations where outputs inadvertently facilitate harmful actions due to overlooked logical implications. Traditional safety solutions, such as scalar outcome-based reward models, parameter tuning, or heuristic decoding strategies, lack the granularity and proactive nature needed to reliably detect and intervene during subtle yet crucial reasoning steps. Addressing this fundamental gap, we introduce AURA, an innovative, multi-layered framework centered around Process Reward Models (PRMs), providing comprehensive, step level evaluations across logical coherence and safety-awareness. Our framework seamlessly combines introspective self-critique, fine-grained PRM assessments, and adaptive safety-aware decoding to dynamically and proactively guide models toward safer reasoning trajectories. Empirical evidence clearly demonstrates that this approach significantly surpasses existing methods, significantly improving the logical integrity and affordance-sensitive safety of model outputs. This research represents a pivotal step toward safer, more responsible, and contextually aware AI, setting a new benchmark for alignment-sensitive applications.

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TMLR Journal 2025 Journal Article

MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation

Sayantan Adak
Somnath Banerjee
Rajarshi Mandal
Avik Halder
Sayan Layek
Rima Hazra
Animesh Mukherjee

Online memes are a powerful yet challenging medium for content moderation, often masking harmful intent behind humor, irony, or cultural symbolism. Conventional moderation systems “especially those relying on explicit text” frequently fail to recognize such subtle or implicit harm. We introduce MemeSense, an adaptive framework designed to generate socially grounded interventions for harmful memes by combining visual and textual understanding with curated, semantically aligned examples enriched with commonsense cues. This enables the model to detect nuanced complexed threats like misogyny, stereotyping, or vulgarity “even in memes lacking overt language”. Across multiple benchmark datasets, MemeSense outperforms state-of-the-art methods, achieving up to 35% higher semantic similarity and 9% improvement in BERTScore for non-textual memes, and notable gains for text-rich memes as well. These results highlight MemeSense as a promising step toward safer, more context-aware AI systems for real-world content moderation. The code and data are available at: https://github.com/sayantan11995/MemeSense

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AAAI Conference 2025 Conference Paper

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

Somnath Banerjee
Sayan Layek
Soham Tripathy
Shanu Kumar
Animesh Mukherjee
Rima Hazra

Language models aligned for safety often exhibit fragile and imbalanced mechanisms, increasing the chances of producing unsafe content. In addition, editing techniques to incorporate new knowledge can further compromise safety. To tackle these issues, we propose SafeInfer, a context-adaptive, decoding-time safety alignment strategy for generating safe responses to user queries. safeInfer involves two phases: the 'safety amplification' phase, which uses safe demonstration examples to adjust the model’s hidden states and increase the likelihood of safer outputs, and the 'safety-guided decoding' phase, which influences token selection based on safety-optimized distributions to ensure the generated content adheres to ethical guidelines. Further, we introduce HarmEval, a novel benchmark for comprehensive safety evaluations, designed to address potential misuse scenarios in line with the policies of leading AI technology companies.

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IJCAI Conference 2022 Conference Paper

CounterGeDi: A Controllable Approach to Generate Polite, Detoxified and Emotional Counterspeech

Punyajoy Saha
Kanishk Singh
Adarsh Kumar
Binny Mathew
Animesh Mukherjee

Recently, many studies have tried to create generation models to assist counter speakers by providing counterspeech suggestions for combating the explosive proliferation of online hate. However, since these suggestions are from a vanilla generation model, they might not include the appropriate properties required to counter a particular hate speech instance. In this paper, we propose CounterGeDi - an ensemble of generative discriminators (GeDi) to guide the generation of a DialoGPT model toward more polite, detoxified, and emotionally laden counterspeech. We generate counterspeech using three datasets and observe significant improvement across different attribute scores. The politeness and detoxification scores increased by around 15% and 6% respectively, while the emotion in the counterspeech increased by at least 10% across all the datasets. We also experiment with triple-attribute control and observe significant improvement over single attribute results when combining complementing attributes, e. g. , politeness, joyfulness and detoxification. In all these experiments, the relevancy of the generated text does not deteriorate due to the application of these controls.

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NeurIPS Conference 2022 Conference Paper

Multilingual Abusive Comment Detection at Scale for Indic Languages

Vikram Gupta
Sumegh Roychowdhury
Mithun Das
Somnath Banerjee
Punyajoy Saha
Binny Mathew
hastagiri prakash vanchinathan
Animesh Mukherjee

Social media platforms were conceived to act as online town squares' where people could get together, share information and communicate with each other peacefully. However, harmful content borne out of bad actors are constantly plaguing these platforms slowly converting them into mosh pits' where the bad actors take the liberty to extensively abuse various marginalised groups. Accurate and timely detection of abusive content on social media platforms is therefore very important for facilitating safe interactions between users. However, due to the small scale and sparse linguistic coverage of Indic abusive speech datasets, development of such algorithms for Indic social media users (one-sixth of global population) is severely impeded. To facilitate and encourage research in this important direction, we contribute for the first time MACD - a large-scale (150K), human-annotated, multilingual (5 languages), balanced (49\% abusive content) and diverse (70K users) abuse detection dataset of user comments, sourced from a popular social media platform - ShareChat. We also release AbuseXLMR, an abusive content detection model pretrained on large number of social media comments in 15+ Indic languages which outperforms XLM-R and MuRIL on multiple Indic datasets. Along with the annotations, we also release the mapping between comment, post and user id's to facilitate modelling the relationship between them. We share competitive monolingual, cross-lingual and few-shot baselines so that MACD can be used as a dataset benchmark for future research.

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AAAI Conference 2021 Conference Paper

HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection

Binny Mathew
Punyajoy Saha
Seid Muhie Yimam
Chris Biemann
Pawan Goyal
Animesh Mukherjee

Hate speech is a challenging issue plaguing the online social media. While better models for hate speech detection are continuously being developed, there is little research on the bias and interpretability aspects of hate speech. In this paper, we introduce HateXplain, the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in our dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i. e. , hate, offensive or normal), the target community (i. e. , the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i. e. , the portions of the post on which their labelling decision (as hate, offensive or normal) is based. We utilize existing state-of-the-art models and observe that even models that perform very well in classification do not score high on explainability metrics like model plausibility and faithfulness. We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities. We have made our code and dataset public1 for other researchers2.

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AAMAS Conference 2018 Conference Paper

ComPAS: Community Preserving Sampling for Streaming Graphs

Sandipan Sikdar
Tanmoy Chakraborty
Soumya Sarkar
Niloy Ganguly
Animesh Mukherjee

In the era of big data, graph sampling is indispensable in many settings. Existing sampling methods are mostly designed for static graphs, and aim to preserve basic structural properties of the original graph (such as degree distribution, clustering coefficient etc.) in the sample. We argue that for any sampling method it is impossible to produce an universal representative sample which can preserve all the properties of the original graph; rather sampling should be application specific (such as preserving hubs - needed for information diffusion). Here we consider community detection as an application scenario. We propose ComPAS, a novel sampling strategy that unlike previous methods, is not only designed for streaming graphs (which is a more realistic representation of a real-world scenario) but also preserves the community structure of the original graph in the sample. Empirical results on both synthetic and different real-world graphs show that ComPAS is the best to preserve the underlying community structure with average performance reaching 73. 2% of the most informed algorithm for static graphs.

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