Papers by Eshaan Tanwar
Language Models can Exploit Cross-Task In-context Learning for Data-Scarce Novel Tasks (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have transformed NLP with their remarkable In-context Learning capabilities. |
| Approach: | They propose to use large language models to generalize from labeled examples of predefined tasks to novel tasks . they use biological neurons and the Transformer architecture to study the potential for information sharing across tasks. |
| Outcome: | The proposed model can generalize from labeled examples of predefined tasks to novel tasks despite no examples from the target task in the context. |
Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment (2023.acl-long)
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| Challenge: | a handful of studies have explored ICL in a cross-lingual setting . emergence of large-scale, pretrained, Transformer-based language models has marked the commencement of an avant-garde era in NLP. |
| Approach: | They propose a novel prompt construction strategy to bridge the gap between ICL and cross-lingual text classification. |
| Outcome: | The proposed approach outperforms random prompt selection by a large margin across three tasks using 44 different cross-lingual pairs. |
Do You Know About My Nation? Investigating Multilingual Language Models’ Cultural Literacy Through Factual Knowledge (2025.emnlp-main)
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| Challenge: | Multilingual question-answering benchmarks do not factor in regional diversity in the information they capture and tend to be Western-centric. |
| Approach: | They propose to benchmark eight standard multilingual LLMs on XNationQA and evaluate them using two novel transference metrics. |
| Outcome: | The proposed model shows greater knowledge of cultural information in English than in the dominant language of the respective culture. |