Papers by Neel Kant
HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM (2024.naacl-long)
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Zhilin Wang, Yi Dong, Jiaqi Zeng, Virginia Adams, Makesh Narsimhan Sreedhar, Daniel Egert, Olivier Delalleau, Jane Scowcroft, Neel Kant, Aidan Swope, Oleksii Kuchaiev
| Challenge: | Existing helpfulness preference datasets do not specify what makes some responses more helpful and others less helpful. |
| Approach: | They use a dataset that has annotated for correctness, coherence, complexity, and verbosity. |
| Outcome: | The dataset has annotations for correctness, coherence, complexity, and verbosity in addition to overall helpfulness of responses. |
End-to-End Training of Neural Retrievers for Open-Domain Question Answering (2021.acl-long)
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Devendra Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L. Hamilton, Bryan Catanzaro
| Challenge: | Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised methods. |
| Approach: | They propose an approach of unsupervised pre-training with the Inverse Cloze Task and masked salient spans followed by supervised finetuning using question-context pairs. |
| Outcome: | The proposed approach outperforms models like REALM and RAG in retrieval accuracy and answer extraction. |