Papers by Sachin Mehta
Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text (2021.emnlp-main)
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Christopher Clark, Jordi Salvador, Dustin Schwenk, Derrick Bonafilia, Mark Yatskar, Eric Kolve, Alvaro Herrasti, Jonghyun Choi, Sachin Mehta, Sam Skjonsberg, Carissa Schoenick, Aaron Sarnat, Hannaneh Hajishirzi, Aniruddha Kembhavi, Oren Etzioni, Ali Farhadi
| Challenge: | Communicating with humans is challenging for AIs because of its complexity and multimodality. |
| Approach: | They propose to use a game of drawing and guessing based on Pictionary to test AIs' understanding of the world and multi-modal gestures. |
| Outcome: | The proposed game is a test for mixing language and visual/symbolic communication in AI. |
MedICaT: A Dataset of Medical Images, Captions, and Textual References (2020.findings-emnlp)
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Sanjay Subramanian, Lucy Lu Wang, Ben Bogin, Sachin Mehta, Madeleine van Zuylen, Sravanthi Parasa, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi
| Challenge: | Existing largescale datasets explicitly exclude compound figures . existing systems lack this ability to identify relevant subfigures . |
| Approach: | They propose a dataset of medical images in context that allows figure-to-text alignment . they use captions, inline references and manually annotated subfigures for compound figures . |
| Outcome: | The proposed dataset demonstrates the utility of inline references in image-text matching. |
TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining (2025.acl-long)
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Jeffrey Li, Mohammadreza Armandpour, Seyed Iman Mirzadeh, Sachin Mehta, Vaishaal Shankar, Raviteja Vemulapalli, Samy Bengio, Oncel Tuzel, Mehrdad Farajtabar, Hadi Pouransari, Fartash Faghri
| Challenge: | Large language models (LLMs) trained on historical web data inevitably become outdated. |
| Approach: | They propose a web-scale dataset for time-continual pretraining of LLMs derived from 114 dumps of Common Crawl (CC) they also design time-stratified evaluations to assess how well various continual learning methods adapt to new data while retaining past knowledge. |
| Outcome: | The proposed benchmarks show that autoregressive meta-schedules combined with a fixed-ratio replay of older data can achieve comparable held-out loss to re-training from scratch, while requiring significantly less computation (2.6x). |
SHARCS: Efficient Transformers Through Routing with Dynamic Width Sub-networks (2023.findings-emnlp)
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| Challenge: | Several methods have been proposed to improve the inference efficiency of transformer-based models. |
| Approach: | They propose a new adaptive inference method that takes into account the hardness of input samples. |
| Outcome: | The proposed model outperforms or complements existing per-sample adaptive inference methods in terms of accuracy vs. FLOPs and can be applied to compressed and efficient transformer encoders to further improve their efficiency. |
Pyramidal Recurrent Unit for Language Modeling (D18-1)
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| Challenge: | Long short term memory units are powerful tools for language modeling, but their performance can be limited by the number of parameters. |
| Approach: | They propose a pyramidal recurrent unit which enables learning representations in high dimensional space with more generalization power and fewer parameters. |
| Outcome: | The proposed model outperforms existing models with different gating mechanisms and transformations on word-level language modeling tasks. |