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Sailik Sengupta

As seen in: Amazon Science
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Articles

A better training method for reinforcement learning with human feedback

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By Sailik Sengupta, Saket Dingliwal
| Amazon Science
Reinforcement learning with human feedback (RLHF) is the standard method for aligning large language models (LLMs) with human preferences — such as the preferences for nontoxic language and factually accurate responses. Recently, one of the most popular RLHF methods has been direct preference optimization, in which the LLM chooses between two output options, one of which has been labeled as preferred by human annotators.
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InsTALL: Context-aware Instructional Task Assistance with Multi-modal Large Language Models

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By Sailik Sengupta, Girik Malik, Arshit Gupta, Bonan Min
| arxiv.org
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SeRA: Self-Reviewing and Alignment of Large Language Models using Implicit Reward Margins

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By Saket Dingliwal, Sailik Sengupta, Sravan Bodapati, Bhavana Ganesh
| arxiv.org
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