Papers by Dushyanta Dhyani
Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context (2026.acl-long)
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Yilun Zhu, Yuan Zhuang, Nikhita Vedula, Dushyanta Dhyani, Shaoyuan Xu, Mohsen Bayati, Bryan Wang, Shervin Malmasi
| Challenge: | Existing methods for text regression lack local grounding and rely on shared representations. |
| Approach: | They propose a distributional regression model with quantile tokens that insert dedicated quantiles into the input sequence. |
| Outcome: | The proposed method outperforms baseline models on the inside Airbnb and StackSample datasets. |
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction (2026.findings-acl)
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| Challenge: | Existing methods for text generation require multiple independent sequences to be decoded in parallel. |
| Approach: | They propose an algorithm that accelerates offline decoding by leveraging shared memory and computation across batches. |
| Outcome: | Experiments show that attribute-value pairs are conditionally independent, enabling decoding in parallel up to 96 tokens per prompt. |
Quantile Regression with Large Language Models for Price Prediction (2025.findings-acl)
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| Challenge: | Existing approaches to structured prediction tasks focus on point estimates and lack systematic comparison across different methods. |
| Approach: | They propose a novel quantile regression approach that enables LLMs to produce full predictive distributions, improving upon traditional point estimates. |
| Outcome: | The proposed model outperforms encoder architectures, embedding-based methods, and few-shot learning methods in prediction accuracy and distributional calibration. |