Neel Kant


2024

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HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM
Zhilin Wang | Yi Dong | Jiaqi Zeng | Virginia Adams | Makesh Narsimhan Sreedhar | Daniel Egert | Olivier Delalleau | Jane Scowcroft | Neel Kant | Aidan Swope | Oleksii Kuchaiev
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Existing open-source helpfulness preference datasets do not specify what makes some responses more helpful and others less so. Models trained on these datasets can incidentally learn to model dataset artifacts (e.g. preferring longer but unhelpful responses only due to their length). To alleviate this problem, we collect HelpSteer, a multi-attribute helpfulness dataset annotated for the various aspects that make responses helpful. Specifically, our 37k-sample dataset has annotations for correctness, coherence, complexity, and verbosity in addition to overall helpfulness of responses. Training Llama 2 70B using the HelpSteer dataset with SteerLM technique produces a model that scores 7.54 on MT Bench, which is currently the highest score for open models that do not require training data from more powerful models (e.g. GPT-4). We release this dataset with CC-BY-4.0 license at https://huggingface.co/datasets/nvidia/HelpSteer

2021

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End-to-End Training of Neural Retrievers for Open-Domain Question Answering
Devendra Sachan | Mostofa Patwary | Mohammad Shoeybi | Neel Kant | Wei Ping | William L. Hamilton | Bryan Catanzaro
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsupervised and supervised methods can be used most effectively for neural retrievers. In this work, we systematically study retriever pre-training. We first propose an approach of unsupervised pre-training with the Inverse Cloze Task and masked salient spans, followed by supervised finetuning using question-context pairs. This approach leads to absolute gains of 2+ points over the previous best result in the top-20 retrieval accuracy on Natural Questions and TriviaQA datasets. We next explore two approaches for end-to-end training of the reader and retriever components in OpenQA models, which differ in the manner the reader ingests the retrieved documents. Our experiments demonstrate the effectiveness of these approaches as we obtain state-of-the-art results. On the Natural Questions dataset, we obtain a top-20 retrieval accuracy of 84%, an improvement of 5 points over the recent DPR model. We also achieve good results on answer extraction, outperforming recent models like REALM and RAG by 3+ points.