Papers by Ronak Pradeep
How Does Generative Retrieval Scale to Millions of Passages? (2023.emnlp-main)
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Ronak Pradeep, Kai Hui, Jai Gupta, Adam Lelkes, Honglei Zhuang, Jimmy Lin, Donald Metzler, Vinh Tran
| Challenge: | generative retrieval is a new paradigm for information retrieval, enabling a sequence-to-sequence model with a single Transformer . generative encoders have been used on small corpora, but only on large ones . |
| Approach: | They propose to encode an entire document corpus within a single Transformer . they find generative retrieval is competitive with state-of-the-art dual encoders on small corpora . |
| Outcome: | The proposed approach is competitive with state-of-the-art dual encoders on small corpora, the study finds . the proposed approach only evaluates on document corporales on the order of 100K in size . |
ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA Datasets with Large Language Models (2024.emnlp-industry)
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Ronak Pradeep, Daniel Lee, Ali Mousavi, Jeffrey Pound, Yisi Sang, Jimmy Lin, Ihab Ilyas, Saloni Potdar, Mostafa Arefiyan, Yunyao Li
| Challenge: | Knowledge Graphs (KGs) are a powerful tool for capturing structured representations of the world. |
| Approach: | They propose a scalable method for generating up-to-date and configurable conversational KGQA datasets that adheres to human interaction configurations and operates at a significantly larger scale. |
| Outcome: | Qualitative psychometric analyses show that ConvKGYarn produces high-quality data comparable to popular conversational KGQA datasets across various metrics. |
Entity Disambiguation via Fusion Entity Decoding (2024.naacl-long)
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Junxiong Wang, Ali Mousavi, Omar Attia, Ronak Pradeep, Saloni Potdar, Alexander Rush, Umar Farooq Minhas, Yunyao Li
| Challenge: | Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark. |
| Approach: | They propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions. |
| Outcome: | The proposed model outperforms existing classification models on the ZELDA benchmark and on retrieval/reader frameworks. |
Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages (2024.acl-short)
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| Challenge: | Large language models (LLMs) have shown impressive zero-shot capabilities in various passage ranking tasks. |
| Approach: | They analyze and compare the effectiveness of monolingual reranking using query or document translations and evaluate the effectiveness when leveraging their own generated translations. |
| Outcome: | The proposed models perform better when using their own translations than when using query or document translations. |
Document Ranking with a Pretrained Sequence-to-Sequence Model (2020.findings-emnlp)
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| Challenge: | Experimental results on the MS MARCO passage ranking task show that our ranking approach is superior to strong encoder-only models. |
| Approach: | They propose to use a pretrained sequence-to-sequence model to generate relevance labels as "target tokens" they also show how the underlying logits of these target tokens can be interpreted as relevance probabilities for ranking. |
| Outcome: | The proposed model outperforms existing models in a data-poor setting and significantly outperformed an encoder-only model on the MS MARCO passage ranking task. |
Exploring Listwise Evidence Reasoning with T5 for Fact Verification (2021.acl-short)
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| Challenge: | Existing methods for fact verification use pretrained sequence-to-sequence transformers for sentence selection and label prediction. |
| Approach: | They propose a framework for fact verification that leverages pretrained sequence-to-sequence transformer models for sentence selection and label prediction. |
| Outcome: | The proposed framework scores higher than the second place approach on the blind test set . the proposed framework can be useful for a broader range of NLP tasks, the authors say . |