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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…