Papers by Amit Sharma
Task Facet Learning: A Structured Approach To Prompt Optimization (2025.findings-acl)
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| Challenge: | Existing approaches to prompt optimization are limited to learning multiple facets of a task from training examples. |
| Approach: | They propose to optimize a text prompt by considering different facets of a task and including them in the prompt. |
| Outcome: | The proposed algorithm can generate long, complex prompts that existing methods are unable to generate. |
YinYang-Align: A new Benchmark for Competing Objectives and Introducing Multi-Objective Preference based Text-to-Image Alignment (2025.findings-acl)
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Amitava Das, Yaswanth Narsupalli, Gurpreet Singh, Vinija Jain, Vasu Sharma, Suranjana Trivedy, Aman Chadha, Amit Sheth
| Challenge: | Recent controversies highlight the need for robust alignment mechanisms in text-to-image systems. |
| Approach: | They propose a framework to evaluate T2I systems across six contradictory alignment objectives . objectives highlight key trade-offs such as artistic freedom and cultural sensitivity . |
| Outcome: | The proposed framework achieves superior alignment across all objectives. |
Counter Turing Test (CT2): AI-Generated Text Detection is Not as Easy as You May Think - Introducing AI Detectability Index (ADI) (2023.emnlp-main)
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Megha Chakraborty, S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Shreya Gautam, Tanay Kumar, Krish Sharma, Niyar Barman, Chandan Gupta, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | a number of issues have arisen regarding the risk and consequences of AI-generated text detection. |
| Approach: | They propose a counter-turing test to evaluate the robustness of existing AGTD methods . they propose ADI, a quantifiable spectrum to assess detectability of LLMs . |
| Outcome: | The proposed method evaluates the robustness of existing AGTD methods . it shows that larger LLMs tend to have lower ADI, indicating they are less detectable . |
HORIZON: A Benchmark for In-the-wild User Behaviour Modeling (2026.findings-acl)
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| Challenge: | Existing user modeling benchmarks focus on short sessions and next-item prediction within a single domain. |
| Approach: | They propose a benchmark that reformulates user modeling along three axes . it covers 54M users and 35M items, enabling pretraining and evaluation . they propose tasks and evaluation setups that better reflect real-world deployment scenarios . |
| Outcome: | The proposed benchmark covers 54M users and 35M items, and is based on Amazon Reviews. |
Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers (2023.acl-long)
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| Challenge: | toxicity and IMDB review datasets show that pre-trained NLP classifiers learn spurious correlations between input features and label . |
| Approach: | They propose an algorithm to regularize the learnt effect of features on the model’s prediction to the estimated effect of a feature on label. |
| Outcome: | The proposed method minimises spurious correlations and improves minority group accuracy while improving total accuracy compared to standard training. |
Evaluating the Effectiveness and Scalability of LLM-Based Data Augmentation for Retrieval (2025.emnlp-main)
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| Challenge: | Existing research does not explore key factors such as optimal augmentation scale and the necessity of using large augmentation models. |
| Approach: | They propose to use LLMs to augment compact dual-encoder models to improve retrieval performance. |
| Outcome: | The proposed approach improves retrieval performance but its benefits diminish beyond a certain scale even with diverse augmentation strategies. |
NICE: To Optimize In-Context Examples or Not? (2024.acl-long)
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| Challenge: | Recent work shows that in-context learning and optimization of in-const examples (ICE) can improve the accuracy of large language models on a wide range of tasks. |
| Approach: | They propose a task-specific metric called Normalized Invariability to Choice of Examples (NICE) metric measures the learnability of tasks from a given instruction and provides a heuristic to decide whether to optimize ICE for a new task. |
| Outcome: | The proposed metric predicts the utility of optimizing ICE for a given task compared to random ICE. |
Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi (2020.lrec-1)
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Devansh Mehta, Sebastin Santy, Ramaravind Kommiya Mothilal, Brij Mohan Lal Srivastava, Alok Sharma, Anurag Shukla, Vishnu Prasad, Venkanna U, Amit Sharma, Kalika Bali
| Challenge: | 40% of all the languages in the world face the danger of extinction in the near future . when a language dies out, future generations lose a vital part of the culture that is necessary to completely understand it. |
| Approach: | They propose to use 4 technology-driven methods of data collection to collect data on Gondi, a low-resource vulnerable language spoken by 2.3 million tribal people in south and central India. |
| Outcome: | The proposed methods collected 12,000 translated words and/or sentences and identified more than 650 community members whose help can be solicited for future translation efforts. |