Papers by Arkil Patel
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks (2022.emnlp-main)
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| Challenge: | Recent benchmarks like ReaSCAN use navigation tasks grounded in a grid world to assess whether neural models exhibit compositional behaviour. |
| Approach: | They propose a transformer-based model that outperforms specialized architectures on ReaSCAN and a modified version of gSCAN to test their performance. |
| Outcome: | The proposed model outperforms specialized architectures on ReaSCAN and gSCAN on a grid world and can generalize to deeper input structures. |
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have a knowledge cutoff and are costly to finetune repeatedly. |
| Approach: | They introduce a language evaluation suite that incorporates diverse tokens and prompt settings to simulate real-world complexity. |
| Outcome: | The proposed evaluation suite incorporates diverse tokens and prompt settings to simulate real-world complexity. |
Are NLP Models really able to Solve Simple Math Word Problems? (2021.naacl-main)
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| Challenge: | Existing solvers for math word problems often achieve high performance on benchmark datasets . existing models rely on shallow heuristics to achieve high accuracy . |
| Approach: | They restrict their attention to English MWPs taught in grades four and lower . they propose a challenge dataset to test the accuracy of MWp solvers . |
| Outcome: | The proposed model can solve a large fraction of MWPs even with shallow heuristics . the proposed model is much lower on the challenge dataset SVAMP . |
Revisiting the Compositional Generalization Abilities of Neural Sequence Models (2022.acl-short)
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| Challenge: | Existing studies have suggested that standard seq-to-seq models lack the ability to generalize compositionally. |
| Approach: | They propose to use one-shot primitive generalization as introduced by the popular SCAN benchmark to modify the training distribution in simple and intuitive ways to achieve near-perfect generalization performance. |
| Outcome: | The proposed model achieves near-perfect generalization performance despite a lack of training data . |
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions (2023.acl-long)
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| Challenge: | Recent studies have found that Transformers struggle to model several formal languages when compared to recurrent models. |
| Approach: | They conduct an extensive empirical study on Boolean functions to demonstrate that Transformers are relatively more biased towards functions of low sensitivity . they also show that Transformer's generalize near perfectly even in the presence of noisy labels whereas recurrent models overfit and achieve poor generalization accuracy. |
| Outcome: | The results show that Transformers generalize near perfectly even in noisy Boolean functions whereas recurrent models overfit and achieve poor generalization accuracy. |
Evaluating In-Context Learning of Libraries for Code Generation (2024.naacl-long)
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| Challenge: | Recent work shows that large proprietary LLMs can learn novel library usage in-context from demonstrations. |
| Approach: | They evaluate large proprietary LLMs to understand library usage in-context . they find they are able to generate code based on library specification presented in-constext - a promising area . |
| Outcome: | The proposed models can learn library usage in-context from demonstrations . the results pave the way for more adaptable and dynamic coding environments. |