Papers by Sourangshu Bhattacharya
EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning (2024.emnlp-main)
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Kiran Purohit, Venktesh V, Raghuram Devalla, Krishna Yerragorla, Sourangshu Bhattacharya, Avishek Anand
| Challenge: | Recent advances in large language models (LLMs) have enabled in-context learning (ICL) a critical challenge in ICL is the selection of optimal exemplars . |
| Approach: | They propose an algorithm for static exemplar subset selection for reasoning tasks . they propose a method that estimates parameters without incorporating confidence information . |
| Outcome: | The proposed method significantly reduces the number of LLM calls to 11% of those required by state-of-the-art methods and achieves a substantial performance improvement of 12.24%. |
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for tagging opinion triplets fail to capture the strong interdependence between the three opinion factors, whereas grid tabbing fails to capture span-level semantics while predicting sentiment between an aspect-opinion pair. |
| Approach: | They propose a tagging-free approach to extracting opinion triplets using a pointer network decoding framework that captures the interdependence between the three elements of an opinion triple. |
| Outcome: | The proposed architecture captures the interdependence between the aspect and opinion triplets while predicting their connecting sentiment. |