8 citations · 10 across the 6 of their papers we have counts for
6 papers
Characterizing Deep Research: A Benchmark and Formal Definition
Abhinav Java, Ashmit Khandelwal, Sukruta Midigeshi +6
Information tasks such as writing surveys or analytical reports require complex search and reasoning, and have recently been grouped under the umbrella of \textit{deep research} --…
Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation
Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha +3
We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation. While exis…
Task Facet Learning: A Structured Approach to Prompt Optimization
Gurusha Juneja, Gautam Jajoo, Nagarajan Natarajan +3
Given a task in the form of a basic description and its training examples, prompt optimization is the problem of synthesizing the given information into a text prompt for a large l…
Provably Robust DPO: Aligning Language Models with Noisy Feedback
Sayak Ray Chowdhury, Anush Kini, Nagarajan Natarajan
Learning from preference-based feedback has recently gained traction as a promising approach to align language models with human interests. While these aligned generative models ha…
GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval
Daman Arora, Anush Kini, Sayak Ray Chowdhury +3
Given a query and a document corpus, the information retrieval (IR) task is to output a ranked list of relevant documents. Combining large language models (LLMs) with embedding-bas…
Differentially Private Reward Estimation with Preference Feedback
Sayak Ray Chowdhury, Xingyu Zhou, Nagarajan Natarajan
Learning from preference-based feedback has recently gained considerable traction as a promising approach to align generative models with human interests. Instead of relying on num…