Rajarshi Das
6 papers in the PaperMetrix corpus
Papers by this author
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Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
2019 · arXiv (Cornell University)
This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to the architecture of the machine reading model, only …
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Do Multi-hop Readers Dream of Reasoning Chains?
2019 · arXiv (Cornell University)
General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to derive the answer. In this paper we conduct a systematic analysis …
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ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
2020
Given questions regarding some prototypical situation -such as Name something that people usually do before they leave the house for work? -a human can easily answer them via acquired experiences. There can be multiple right …
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Exploring Next Token Prediction in Theory of Mind (ToM) Tasks: Comparative Experiments with GPT-2 and LLaMA-2 AI Models
2025 · arXiv (Cornell University)
Language models have made significant progress in generating coherent text and predicting next tokens based on input prompts. This study compares the next-token prediction performance of two well-known models: OpenAI's GPT-2 and Meta's Llama-2-7b-chat-hf on …
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Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
2017 · arXiv (Cornell University)
Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new …
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Case-based Reasoning for Natural Language Queries over Knowledge Bases
2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay Yoon Lee, Lizhen Tan, Lazaros Polymenakos, Andrew McCallum. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.