Papers by Karthik Raman
Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning (2024.naacl-long)
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| Challenge: | Recent advances of Large Language Models (LLMs) have been pushing the field of Natural Language Processing (NLP) to the next level in many different aspects. |
| Approach: | They propose a novel labeling method which estimates how much incremental knowledge is brought into LLMs by a demonstration. |
| Outcome: | The proposed method estimates how much incremental knowledge is brought into the LLMs by a demonstration. |
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation (2022.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) have shown impressive results on a variety of text understanding tasks. |
| Approach: | They propose a two-stage distillation approach that allows retrieval augmentation to be carried over without the increased compute associated with it. |
| Outcome: | The proposed approach can carry over the gains of retrieval augmentation without suffering the increased compute typically associated with it. |
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling (2020.findings-emnlp)
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Jiecao Chen, Liu Yang, Karthik Raman, Michael Bendersky, Jung-Jung Yeh, Yun Zhou, Marc Najork, Danyang Cai, Ehsan Emadzadeh
| Challenge: | Existing knowledge distillation models are not optimized for dealing with pairs (or tuples) of texts. |
| Approach: | They propose a framework for distilling fast and accurate models on text pair tasks using a scalable end-to-end training strategy. |
| Outcome: | Empirical studies on academic and real-world e-commerce benchmarks show the proposed framework can achieve speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model. |
Transforming Sequence Tagging Into A Seq2Seq Task (2022.emnlp-main)
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| Challenge: | Pretrained, large, generative language models have had great success in a wide range of sequence tagging and structured prediction tasks. |
| Approach: | They propose to use a new format for casting input text sentences and their output labels into the input and target of a Seq2Seq model and introduce it to test their hypothesis. |
| Outcome: | The proposed format shows to be both simpler and more effective and devoid of hallucination. |
It’s All Relative! – A Synthetic Query Generation Approach for Improving Zero-Shot Relevance Prediction (2024.findings-naacl)
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| Challenge: | Large language models generate synthetic query-document pairs by prompting with as few as 8 demonstrations. |
| Approach: | They propose to generate queries simultaneously for different labels by prompting with 8 demonstrations. |
| Outcome: | Extensive experimentation shows that synthetic queries generated in such a fashion improve performance. |