Papers by Ishita Ishita
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. |
Machine Translated Text Detection Through Text Similarity with Round-Trip Translation (2021.naacl-main)
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| Challenge: | Existing detectors for translating texts fail to detect a text from a strange translator . Existing methods for detection of translated texts use text structure and complex words to detect translations . |
| Approach: | They propose a detector using text similarity with round-trip translation (TSRT) TSRT achieves 86.9% accuracy in detecting a translated text from a strange translator . Existing detectors have been built around a specific translator but fail to detect a translation from skeptics . |
| Outcome: | Existing detectors fail to detect translated texts from a strange translator . a detector achieves 86.9% accuracy in detecting a translated text from skeptic translators . |
The Evolution of Gen Alpha Slang: Linguistic Patterns and AI Translation Challenges (2025.acl-srw)
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| Challenge: | Generation Alpha (born 2010-2024) exhibits unique linguistic behaviours influenced by rampant online communication and platform-specific cultures. |
| Approach: | They construct a comprehensive slang corpus from online platforms and evaluate four AI translation systems on over 100 sling terms. |
| Outcome: | The proposed translation systems outperform four existing translation models on over 100 slang terms. |
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. |
JEBS: A Fine-grained Biomedical Lexical Simplification Task (2025.findings-acl)
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| Challenge: | Existing systems for simplification of complex medical terms are limited in the scope of their topics and require massive cost and effort to keep up with the latest research. |
| Approach: | They propose a fine-grained lexical simplification task and dataset to enable more targeted development and evaluation of systems for replacing or explaining complex biomedical terms. |
| Outcome: | The proposed task and dataset pave the way for development and evaluation of systems for replacing or explaining complex biomedical terms. |
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 . |
Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination (2024.emnlp-main)
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| Challenge: | a large-scale study of linguistic bias exhibited by ChatGPT covers 10 dialects of English . standard varieties of English, especially SAE, dominate available training data . |
| Approach: | They use ChatGPT to generate models that default to "standard" varieties of English . they also use a feature annotation and native speaker evaluation to analyze the responses . |
| Outcome: | The proposed models default to "standard" varieties of English, but non-"standard" ones exhibit stereotyping, demeaning content, lack of comprehension, condescending responses. |
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. |
Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs (2025.findings-acl)
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| Challenge: | Existing approaches to persona simulation large language models (LLMs) focus on learning basic biographical information, or using limited role-play dialogue datasets to capture a character’s responses. |
| Approach: | They propose to train characters using a linguistic structure and a style-tuning mechanism that allows a general linguistic style expert to collaborate with other task-specific experts to better understand their thoughts. |
| Outcome: | The proposed model outperforms baselines on linguistic accuracy and opinion comprehension on three tasks for Lu Xun's essay collection. |