Papers by Simran Arora
Reasoning over Public and Private Data in Retrieval-Based Systems (2023.tacl-1)
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| Challenge: | Existing retrieval systems assume relevant corpora are fully (e.g., publicly) accessible, but users are often unwilling to expose their private data to entities hosting public data. |
| Approach: | They propose a split iterative retrieval problem involving iterating retrieval over multiple privacy scopes and propose 'concurrentQA' benchmark to test this problem. |
| Outcome: | The proposed method improves on the existing retrieval methods but still suffers performance degradations when applied to a dataset from a public and private distribution. |
Metadata Shaping: A Simple Approach for Knowledge-Enhanced Language Models (2022.findings-acl)
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| Challenge: | Existing methods to capture entity knowledge with factual knowledge are limited . despite its simplicity, metadata shaping is quite effective . |
| Approach: | They propose a method which inserts substrings corresponding to readily available entity metadata into examples at train and inference time based on mutual information. |
| Outcome: | The proposed method exceeds the baseline model by 4.3 F1 points and achieves state-of-the-art results. |
Contextual Embeddings: When Are They Worth It? (2020.acl-main)
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| Challenge: | In recent years, rich contextual embeddings have enabled rapid progress on benchmarks like GLUE, but require significant computational resources during pretraining and during downstream task training and inference. |
| Approach: | They empirically compare contextual embeddings with classic pretrained embedders and a random word embeddable with a simple baseline. |
| Outcome: | The proposed models perform within 5 to 10% accuracy on industry-scale data. |