Papers by Gautam Siddharth Kashyap
When the Model Said ‘No Comment’, We Knew Helpfulness Was Dead, Honesty Was Alive, and Safety Was Terrified (2026.eacl-long)
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| Challenge: | Existing work uses SFT and MoE to align Large Language Models, but these work face challenges in multi-objective settings. |
| Approach: | They propose a framework that uses prompt-injected fine-tuning to extract axis-specific task features . it deploys a MoCaE module that calibrates expert routing using fractal and natural geometry . |
| Outcome: | The proposed framework achieves significant gains on Alpaca, BeaverTails, TruthfulQA and TruthfulQ with +171.5% win rate and +110.1% truthfulness-informativeness. |
AlignCultura: Towards Culturally Aligned Large Language Models? (2026.acl-long)
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| Challenge: | Existing benchmarks represent early steps toward cultural alignment, yet no benchmarks currently enables systematic evaluation of cultural alignment in line with UNESCO’s principles of cultural diversity w.r.t HHH paradigm. |
| Approach: | Align-Cultura aims to evaluate cultural alignment in large language models . it uses a Query Construction pipeline to reclassify prompts and expand underrepresented domains . response generation pairs prompts with culturally grounded responses . |
| Outcome: | Empirically, culturally fine-tuned models improve joint HHH by 4%–6%, reduce cultural failures by 18%, achieve 10%–12% efficiency gains, and limit leakage to 0.3%. |
Are Large Language Models Economically Viable for Industry Deployment? (2026.acl-industry)
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Abdullah Mohammad, Sushant Kumar Ray, Pushkar Arora, Rafiq Ali, Ebad Shabbir, Gautam Siddharth Kashyap, Jiechao Gao, Usman Naseem
| Challenge: | Generative AI is increasingly deployed in healthcare, financial analytics, and conversational automation. |
| Approach: | They propose a framework that evaluates large language models across their full lifecycle on legacy GPUs. |
| Outcome: | The proposed framework evaluates LLMs across their full lifecycle on legacy GPUs. |
Revealing the Truth with ConLLM for Detecting Multi-Modal Deepfakes (2026.findings-eacl)
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Gautam Siddharth Kashyap, Harsh Joshi, Niharika Jain, Ebad Shabbir, Jiechao Gao, Nipun Joshi, Usman Naseem
| Challenge: | Existing methods for deepfake detection suffer from two limitations: modality fragmentation and shallow inter-modal reasoning. |
| Approach: | They propose a framework for multimodal deepfake detection that uses contrastive learning and large language models to mitigate modality fragmentation and refine embeddings to address shallow inter-modal reasoning. |
| Outcome: | ConLLM reduces audio deepfake EER by 50%, improves video accuracy by 8%, and achieves approximately 9% accuracy gains in audio-visual tasks. |
Too Helpful, Too Harmless, Too Honest or Just Right? (2025.emnlp-main)
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| Challenge: | Existing methods optimize for individual alignment dimensions in isolation, leading to trade-offs and inconsistent behavior. |
| Approach: | They propose a modular alignment framework that integrates a Mixture of Calibrated Experts (MoCaE) within the Transformer architecture. |
| Outcome: | The proposed framework outperforms baselines on three alignment benchmarks, achieving 32.5% win rate, 33.9% safety score, and 28.4% truthfulness. |
LLMs on a Budget? Say HOLA (2025.emnlp-industry)
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Zohaib Hasan Siddiqui, Jiechao Gao, Ebad Shabbir, Mohammad Anas Azeez, Rafiq Ali, Gautam Siddharth Kashyap, Usman Naseem
| Challenge: | Current solutions such as quantization, pruning, and Retrieval-Augmented Generation (RAG) offer only partial optimizations and often sacrifice accuracy, speed, or generality. |
| Approach: | They propose an end-to-end optimization framework for efficient LLM deployment . it leverages Hierarchical Speculative Decoding (HSD) for faster inference without quality loss. |
| Outcome: | HOLA delivers +17.6% EMA on GSM8K, +10.5% MCA on ARC, and reduced latency and memory on edge devices like Jetson Nano. |
Do Large Language Models Reflect Demographic Pluralism in Safety? (2026.findings-eacl)
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Usman Naseem, Gautam Siddharth Kashyap, Sushant Kumar Ray, Rafiq Ali, Ebad Shabbir, Abdullah Mohammad
| Challenge: | Existing datasets that focus on demographics and safety are narrow in their annotator pools. |
| Approach: | They propose to decouple value framing from responses by modeling pluralism directly at the prompt level. |
| Outcome: | Demo-SafetyBench decouples value framing from responses to model pluralism at the prompt level. |
Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning? (2026.eacl-industry)
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Sushant Kumar Ray, Gautam Siddharth Kashyap, Sahil Tripathi, Nipun Joshi, Vijay Govindarajan, Rafiq Ali, Jiechao Gao, Usman Naseem
| Challenge: | Clinical Question-Answering (CQA) industry systems rely on Large Language Models (LLMs). |
| Approach: | They propose a framework that applies alignment at inference time rather than through SFT to help CQA users achieve consistent reasoning. |
| Outcome: | MEDASSESS-X improves Accuracy, Factual Consistency and Safety by up to 50%. |
Truth, Trust, and Trouble: Medical AI on the Edge (2025.emnlp-industry)
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Mohammad Anas Azeez, Rafiq Ali, Ebad Shabbir, Zohaib Hasan Siddiqui, Gautam Siddharth Kashyap, Jiechao Gao, Usman Naseem
| Challenge: | Large Language Models (LLMs) are promising for transforming digital health applications . but ensuring they meet industry standards for factual accuracy, usefulness, and safety remains a challenge . |
| Approach: | They present a framework to assess large language models' accuracy, usefulness, and safety . they assess models' honesty, helpfulness, harmlessness and domain-specific tuning . |
| Outcome: | The proposed framework assesses models across honesty, helpfulness, and harmlessness . AlpaCare-13B achieves highest accuracy (91.7%) and harmlessity (0.92) . |