Papers by Shamik Roy
Constrained Decoding with Speculative Lookaheads (2025.naacl-long)
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Nishanth Sridhar Nakshatri, Shamik Roy, Rajarshi Das, Suthee Chaidaroon, Leonid Boytsov, Rashmi Gangadharaiah
| Challenge: | Constrained decoding with lookahead heuristics is effective for aligning LLM generations to human preferences, but the extensive lookaheaded roll-out operations for each generated token make it prohibitively expensive. |
| Approach: | They propose a technique that uses lookaheads to align LLMs to human preferences . they propose 2.2x to 12.15x speedup over greedy decoding . |
| Outcome: | The proposed technique achieves 2.2x to 12.15x speedup over greedy decoding without significant performance reduction. |
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media (2020.emnlp-main)
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| Challenge: | a new study suggests a minimally supervised approach for identifying nuanced political frames in news articles on politically divisive topics. |
| Approach: | They propose a minimally supervised approach for identifying nuanced policy frames in news coverage of politically divisive topics. |
| Outcome: | The proposed subframes can capture differences in political ideology better . the proposed frameworks were tested on immigration, gun control and abortion topics . |
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs (2024.naacl-long)
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| Challenge: | Flow-adhering planning algorithm for task oriented dialogs (TODs) is a task-oriented dialog (TO) that can be used for task planning and API usage. |
| Approach: | They propose a Flow-Adhering Planning algorithm that follows predefined flows and preserves API dependencies in task oriented dialogs. |
| Outcome: | The proposed algorithm outperforms other decoding and prompting-based baselines in task oriented dialogs. |
Hands-On Interactive Neuro-Symbolic NLP with DRaiL (2022.emnlp-demos)
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| Challenge: | Existing methods to enhance and correct NLP models require feedback from users. |
| Approach: | They propose to enhance DRaiL with an easy to use Python interface that allows users to define, modify and augment models, as well as debug and visualize the predictions. |
| Outcome: | The proposed framework supports predicting sentence and entity level moral sentiment in political tweets. |
FairGen: Controlling Sensitive Attributes for Fair Generations in Diffusion Models via Adaptive Latent Guidance (2025.emnlp-main)
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Mintong Kang, Vinayshekhar Bannihatti Kumar, Shamik Roy, Abhishek Kumar, Sopan Khosla, Balakrishnan Murali Narayanaswamy, Rashmi Gangadharaiah
| Challenge: | Text-to-image diffusion models often exhibit generation biases toward specific demographic groups, raising ethical concerns and limiting their adoption. |
| Approach: | They propose an adaptive latent guidance mechanism which controls the generation distribution during inference by dynamically adjusting the diffusion process to enforce specific attributes. |
| Outcome: | The proposed model outperforms existing models on HBE and Stable Bias datasets and achieves substantial bias reduction. |
CAIR: Counterfactual-based Agent Influence Ranker for Agentic AI Workflows (2025.emnlp-main)
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Amit Giloni, Chiara Picardi, Roy Betser, Shamik Bose, Aishvariya Priya Rathina Sabapathy, Roman Vainshtein
| Challenge: | Existing methods to assess the influence of each agent on the AAW’s output perform only static structural analysis, which is unsuitable for inference time execution. |
| Approach: | They propose to use an LLM-based agent influence Ranker to assess the influence level of each agent on the AAW's output and determine which agents are the most influential. |
| Outcome: | The proposed method outperforms baseline methods and produces consistent rankings and relevancy of downstream tasks. |
“A Tale of Two Movements’: Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction (2023.findings-emnlp)
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| Challenge: | a weakly supervised graph-based approach to model #BLM-related tweets is difficult to obtain . |
| Approach: | They propose a weakly supervised graph-based approach that explicitly models perspectives in #BackLivesMatter-related tweets. |
| Outcome: | The proposed model outperforms multitask baselines by a large margin. |
Identifying Morality Frames in Political Tweets using Relational Learning (2021.emnlp-main)
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| Challenge: | Moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. |
| Approach: | They propose a model to predict moral attitudes towards entities and moral foundations jointly using tweets written by US politicians. |
| Outcome: | The proposed model predicts moral attitudes towards entities and moral foundations jointly from tweets written by US politicians. |