Papers by Ishita Dasgupta
A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models (2024.naacl-long)
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| Challenge: | Psychologists have documented several ways in which humans’ inferences deviate from the rules of logic. |
| Approach: | They focus on syllogisms, which are inferences from two simple premises, and show that larger models are more logical than smaller ones. |
| Outcome: | The results show that language models often mimic human biases, but overcome them in some cases. |
Can language models learn from explanations in context? (2022.findings-emnlp)
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Andrew Lampinen, Ishita Dasgupta, Stephanie Chan, Kory Mathewson, Mh Tessler, Antonia Creswell, James McClelland, Jane Wang, Felix Hill
| Challenge: | Language Models can adapt to a few in-context examples, but without training. |
| Approach: | They examine how explanations of few-shot examples can help Language Models (LMs) explanations can improve performance even without tuning, they find . |
| Outcome: | The proposed explanations outperform hand-tuned explanations on small validation sets. |
Plot Twist: Multimodal Models Don’t Comprehend Simple Chart Details (2024.findings-emnlp)
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| Challenge: | Recent advances in multimodal models show remarkable performance in real-world benchmarks for chart and figure understanding like ChartQA that involve interpreting trends, comparing data points, and extracting insights from visuals. |
| Approach: | They propose to ask models basic questions about axes ranges and values to examine their visual understanding abilities in the context of charts. |
| Outcome: | The models perform well on complex tasks, but lack basic capabilities on basic questions. |
The Impact of Depth on Compositional Generalization in Transformer Language Models (2024.naacl-long)
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| Challenge: | In this paper, we test the hypothesis that deeper transformers generalize more compositionally. |
| Approach: | They propose to add layers to transformers to generalize more compositionally . they propose to fine-tune the models so that the total number of parameters is constant . |
| Outcome: | The proposed model generalizes more compositionally than shallower models, but returns diminish . the proposed model can be made shallower without sacrificing performance . |
VISIAR: Empower MLLM for Visual Story Ideation (2025.findings-acl)
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Zhaoyang Xia, Somdeb Sarkhel, Mehrab Tanjim, Stefano Petrangeli, Ishita Dasgupta, Yuxiao Chen, Jinxuan Xu, Di Liu, Saayan Mitra, Dimitris N. Metaxas
| Challenge: | Existing literature on visual storytelling has not explored the ideation process fully. |
| Approach: | They propose a visual story ideation task that automates the selection and arrangement of visual assets into coherent sequences that convey expressive storylines. |
| Outcome: | The proposed framework surpasses baseline by 33.5% and 18.5%, respectively, on three metrics. |