1 citations · 1 across the 9 of their papers we have counts for
10 papers
Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale
Siddharth Gollapudi, Nilesh Gupta, Prasann Singhal +1
Language models (LMs) raise an intriguing alternative to vector-based retrieval: conditioning on an in-context corpus and directly generating a relevant answer. However, prior work…
Compute Allocation for Reasoning-Intensive Retrieval Agents
Sreeja Apparaju, Nilesh Gupta
As agents operate over long horizons, their memory stores grow continuously, making retrieval critical to accessing relevant information. Many agent queries require reasoning-inten…
LUCID: Attention with Preconditioned Representations
Sai Surya Duvvuri, Nirmal Patel, Nilesh Gupta +1
Softmax-based dot-product attention is a cornerstone of Transformer architectures, enabling remarkable capabilities such as in-context learning. However, as context lengths increas…
Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders
Benjamin Rozonoyer, Chong You, Michael Boratko +5
The success of Large Language Models (LLMs) has motivated a shift toward generative approaches to retrieval and ranking, aiming to supersede classical Dual Encoders (DEs) and Cross…
Compressing Many-Shots in In-Context Learning
Devvrit Khatri, Pranamya Kulkarni, Nilesh Gupta +9
Large Language Models (LLMs) have been shown to be able to learn different tasks without explicit finetuning when given many input-output examples / demonstrations through In-Conte…
Scalable In-context Ranking with Generative Models
Nilesh Gupta, Chong You, Srinadh Bhojanapalli +3
In-context Ranking (ICR) is an emerging paradigm for Information Retrieval (IR), which leverages contextual understanding of LLMs by directly incorporating the task description, ca…
LLM-guided Hierarchical Search for End-to-end Reasoning Intensive Retrieval
Nilesh Gupta, Wei-Cheng Chang, Ngot Bui +2
Search systems are increasingly used for reasoning-intensive queries, where what makes a document relevant requires understanding or reasoning over the query-document relation rath…
Exploring Design Choices for Building Language-Specific LLMs
Atula Tejaswi, Nilesh Gupta, Eunsol Choi
Despite rapid progress in large language models (LLMs), their performance on a vast majority of languages remains unsatisfactory. In this paper, we study building language-specific…
Dual-Encoders for Extreme Multi-Label Classification
Nilesh Gupta, Devvrit Khatri, Ankit S Rawat +3
Dual-encoder (DE) models are widely used in retrieval tasks, most commonly studied on open QA benchmarks that are often characterized by multi-class and limited training data. In c…
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval
Ramnath Kumar, Anshul Mittal, Nilesh Gupta +3
Dense embedding-based retrieval is widely used for semantic search and ranking. However, conventional two-stage approaches, involving contrastive embedding learning followed by app…