Papers by Namrata Shivagunde
Down and Across: Introducing Crossword-Solving as a New NLP Benchmark (2022.acl-long)
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| Challenge: | Recent advances in NLP have led to a growing demand for challenging tasks. |
| Approach: | They propose to solve crossword puzzles as a natural language understanding task . they release a corpus of crossword clues from the daily crossword spanning 25 years . |
| Outcome: | The proposed task is based on a corpus of crossword puzzles from the new york times daily crossword spanning 25 years . the dataset contains over half a million unique clue-answer pairs . |
Life after BERT: What do Other Muppets Understand about Language? (2022.acl-long)
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| Challenge: | Existing pre-trained transformer analysis studies focus on one or two model families at a time, overlooking the variability of the architecture and pre-training objectives. |
| Approach: | They utilize oLMpics bench- mark and psycholinguistic probing datasets for a diverse set of 29 models including T5, BART, and ALBERT. |
| Outcome: | The proposed model fails to resolve compositional questions in a zero-shot fashion, suggesting that pre-training objectives are not predictive of a model’s linguistic capabilities. |
Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning (2023.emnlp-main)
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| Challenge: | Language model probing is often used to test specific capabilities of models, but results are limited when benchmarks are small and lack statistical power. |
| Approach: | They extend existing NEG-136 and ROLE-88 benchmarks to 750 sentence pairs and create an extended negation dataset using template-based generation. |
| Outcome: | The proposed datasets show that models perform better on the extended datasets than on the smaller benchmarks. |
Deconstructing In-Context Learning: Understanding Prompts via Corruption (2024.lrec-main)
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| Challenge: | Prior work examined how modifying different elements of the prompt can affect model performance, but this limited number of elements made replication challenging. |
| Approach: | They decompose the entire prompt into four components: task description, demonstration inputs, labels, and inline instructions provided for each demonstration. |
| Outcome: | The proposed model is robust to minor prompt modifications, but its underlying pre-trained backbone is brittle . previous studies focused on models with fewer than 15 billion parameters or exclusively examined black-box models like GPT-3 or PaLM, making replication challenging. |