Papers by Tejas Vaidhya
Logical Fallacy Detection (2022.findings-emnlp)
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Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schoelkopf
| Challenge: | Existing language models perform poorly on logical fallacy detection . fallacious arguments can lead to disagreements, conflicts, endless debates, and a lack of consensus . |
| Approach: | They propose a task of logical fallacy detection and propose LogicClimate to detect fallacies in text. |
| Outcome: | The proposed task outperforms the best language model on Logic and LogicClimate . human reasoning is marred by logical fallacies, and some exacerbate misinformation . |
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP (2021.emnlp-main)
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Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schoelkopf
| Challenge: | a meta-analysis of published studies shows that the causal direction of data collection can explain some trends in NLP . semi-supervised learning and domain adaptation performance differ on a number of tasks . |
| Approach: | They argue that the causal direction of the data collection process has nontrivial implications . authors categorize common NLP tasks according to their causal direction . they also empirically assay the validity of the ICM principle for text data . |
| Outcome: | The proposed model can explain differences in semi-supervised learning and domain adaptation performance across settings. |
Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States (2021.findings-emnlp)
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| Challenge: | Existing studies on political responsiveness focus on long-term policies collected over decades . recent COVID-19 pandemic has given rise to a new political phenomenon, where political leaders make frequent short-term decisions on the same controlled topic. |
| Approach: | They propose to use Twitter data to classify the sentiments toward governors of each state and conduct controlled studies and comparisons. |
| Outcome: | The proposed model focuses on the COVID-19 pandemic, where political leaders make frequent short-term decisions on the same controlled topic. |
Scaling Laws and Efficient Inference for Ternary Language Models (2025.acl-long)
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Tejas Vaidhya, Ayush Kaushal, Vineet Jain, Francis Couture-Harpin, Prashant Shishodia, Majid Behbahani, Yuriy Nevmyvaka, Irina Rish
| Challenge: | Large language models (LLMs) are increasingly used across research and industry applications, yet their inference efficiency remains a challenge. |
| Approach: | They propose ternary language models that employ quantization-aware training to significantly reduce memory requirements. |
| Outcome: | The proposed ternary language models demonstrate sustained performance gains at scale. |