Papers by Srikar Appalaraju
On the Analysis and Distillation of Emergent Outlier Properties in Pre-trained Language Models (2025.naacl-long)
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| Challenge: | Existing studies show that a small subset of dimensions within language Transformers’ representation spaces emerge as "outliers" during pretraining. |
| Approach: | They propose a method that prioritizes critical outlier dimensions in distillation using a weighted MSE loss. |
| Outcome: | The proposed method outperforms state-of-the-art distillation methods and generalizes well across Encoder-only BERT, Decoder-only GPT-2, and Encodeer-Decoder T5 architectures. |
DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder Transformer Models (2024.findings-naacl)
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| Challenge: | Encoder-decoder transformer models suffer from high inference latency due to auto-regressive decoding . Typically, the decoder takes up most of the latency because of the auto-decoding - a problem that is not solved by the current model. |
| Approach: | They propose an approach to perform Dynamic Early Exit on Decoder to reduce inference latency by 20%-74% by using a multi-exit encoder-decoder transformer model trained with deep supervision. |
| Outcome: | The proposed model reduces inference latency by 20%-74% with comparable or even higher accuracy compared to baseline models. |
DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models (2024.emnlp-main)
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Sungnyun Kim, Haofu Liao, Srikar Appalaraju, Peng Tang, Zhuowen Tu, Ravi Satzoda, R. Manmatha, Vijay Mahadevan, Stefano Soatto
| Challenge: | Existing methods for visual document understanding are limited by training on a small-scale, curated document dataset, compromising generalizability of VDU models to diverse documents. |
| Approach: | They propose a framework that integrates external document knowledge into the data generation process. |
| Outcome: | The proposed framework produces high-quality annotations and surpasses direct knowledge distillation approach. |
R-VLM: Region-Aware Vision Language Model for Precise GUI Grounding (2025.findings-acl)
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Joonhyung Park, Peng Tang, Sagnik Das, Srikar Appalaraju, Kunwar Yashraj Singh, R. Manmatha, Shabnam Ghadar
| Challenge: | Existing vision-only GUI agents ground elements from large and cluttered screenshots, requiring them to process substantial irrelevant information that compromises their accuracy. |
| Approach: | They propose a visual agent model for GUI automation that leverages zoomed-in region proposals for precise element localization. |
| Outcome: | The proposed approach improves state-of-the-art grounding accuracy by 13% across diverse GUI platforms on the GUI grounding benchmarks ScreenSpot and AgentStudio. |
A Multi-Modal Multilingual Benchmark for Document Image Classification (2023.findings-emnlp)
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| Challenge: | Existing document image classification datasets have several limitations and we present two new datasets that overcome these limitations. |
| Approach: | They propose to use two newly curated multilingual datasets that overcome these limitations and propose to develop multilingual Document AI models. |
| Outcome: | The proposed datasets overcome limitations in document image classification and open the door for future research into improving Document AI models. |
Turbocharging Web Automation: The Impact of Compressed History States (2025.findings-acl)
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| Challenge: | Existing web automation approaches ignore the importance of history states to accomplish tasks. |
| Approach: | They propose a web history compressor approach to turbocharge web automation using history states by concatenating history states with other inputs. |
| Outcome: | The proposed approach achieves 1.2-5.4% accuracy improvements over baseline methods on Mind2Web and WebLINX datasets. |