Papers by Nemika Tyagi
Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages (2025.findings-emnlp)
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| Challenge: | Existing approaches to analyzing code-switched data are limited in their ability to generalize to multilingual and mixed-language inputs. |
| Approach: | They propose a large-language model-based annotation pipeline to produce UD annotations for code-switched text. |
| Outcome: | The proposed pipeline outperforms existing parsers and baselines in syntactic analysis. |
Chaos with Keywords: Exposing Large Language Models Sycophancy to Misleading Keywords and Evaluating Defense Strategies (2024.findings-acl)
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| Challenge: | sycophancy is a type of hallucination in Large Language Models, which can lead to false information being presented. |
| Approach: | They explore the sycophantic tendencies of Large Language Models where models provide accurate answers even if they are not entirely correct. |
| Outcome: | The proposed models generate factually correct statements even when they are not completely correct. |
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter? (2024.emnlp-main)
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Nemika Tyagi, Mihir Parmar, Mohith Kulkarni, Aswin Rrv, Nisarg Patel, Mutsumi Nakamura, Arindam Mitra, Chitta Baral
| Challenge: | Existing studies evaluate only the final predicted answer of a puzzle, without providing any finer metrics to evaluate them. |
| Approach: | They propose to use a grid-based evaluation dataset to evaluate LLMs' reasoning abilities and a new error taxonomy to evaluate their reasoning chains. |
| Outcome: | The proposed model outperforms existing prompting methods on a wide range of natural language understanding tasks previously thought to be exclusive to humans. |