Papers by Srikanth Doss
Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly deployed in decision-making tasks where accuracy and reliable confidence estimates are essential. |
| Approach: | They propose a calibration-aware reinforcement learning formulation that directly adjusts decision-token probabilities. |
| Outcome: | The proposed model preserves RLVR’s accuracy level while mitigating overconfidence, reducing ECE scores up to 9 points. |
Label Semantics for Few Shot Named Entity Recognition (2022.findings-acl)
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Jie Ma, Miguel Ballesteros, Srikanth Doss, Rishita Anubhai, Sunil Mallya, Yaser Al-Onaizan, Dan Roth
| Challenge: | Named entity recognition (NER) is a fundamental natural language understanding task that requires large amounts of high quality annotated in-domain data. |
| Approach: | They propose a neural architecture that leverages the semantic information in the names of the labels to give the model additional signal and enriched priors. |
| Outcome: | The proposed model is especially effective in low resource settings. |
MEAV: Model Editing with Alignment Vectors for inference time LLM alignment in single and multidomain preference spectrum (2026.findings-acl)
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Sadat Shahriar, Zheng Qi, Nikolaos Pappas, Srikanth Doss, Kishaloy Halder, Monica Sunkara, Manuel Mager, Yassine Benajiba
| Challenge: | Existing training-time alignment methods require full retraining when a change is needed. |
| Approach: | They propose an inference-time model-editing-based alignment method that learns encoded representations of preference dimensions and allows dynamic adjusting of the model behavior. |
| Outcome: | The proposed method can be used to align large language models to human preferences . it reduces the cost of inference by half compared to the prompt engineering approach . |
Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models (2025.findings-acl)
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Qin Liu, Chao Shang, Ling Liu, Nikolaos Pappas, Jie Ma, Neha Anna John, Srikanth Doss, Lluis Marquez, Miguel Ballesteros, Yassine Benajiba
| Challenge: | LLaVA-7B demonstrated a decline in safety alignment ability on multi-modal inputs compared to its LLM backbone. |
| Approach: | They propose a method to recover alignment ability from LLM backbone while preserving functional capabilities of VLMs. |
| Outcome: | The proposed framework recovers alignment ability that is inherent in the LLM backbone with minimal impact on fluency and linguistic capabilities of pre-trained VLMs. |
The Subtle Art of Defection: Understanding Uncooperative Behaviors in LLM based Multi-Agent Systems (2026.eacl-industry)
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| Challenge: | Existing literature on uncooperative behavior degrades collective outcomes and requires more resilient multi-agent systems. |
| Approach: | They propose a game theory-based taxonomy of uncooperative agent behaviors and a structured, multi-stage simulation pipeline that dynamically generates and refines uncooperation behaviors as agents’ states evolve. |
| Outcome: | The proposed framework achieves 96.7% accuracy in generating realistic uncooperative behaviors, validated by human evaluations. |