12 citations · 24 across the 15 of their papers we have counts for
15 papers
Quantifying and Expanding the Theoretical Capacity of Late-Interaction Retrieval Models
Julian Killingback, Varad Ingale, Hamed Zamani +1
Late-interaction retrieval models that use the MaxSim similarity function have shown strong empirical performance, often outperforming single-vector dense and sparse retrieval mode…
RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents
Mingchen Li, Hansi Zeng, Zhuo Qian +4
Retrieval is increasingly moving from one-shot matching toward interactive reasoning, where language agents iteratively inspect evidence, reformulate queries, and search again. Tra…
CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search
Hansi Zeng, Liam Collins, Bhuvesh Kumar +2
Agentic search -- the task of training agents that iteratively reason, issue queries, and synthesize retrieved information to answer complex questions -- has achieved remarkable pr…
TARSE: Test-Time Adaptation via Retrieval of Skills and Experience for Reasoning Agents
Junda Wang, Zonghai Tao, Hansi Zeng +3
Complex clinical decision making often fails not because a model lacks facts, but because it cannot reliably select and apply the right procedural knowledge and the right prior exa…
Scaling Laws for Embedding Dimension in Information Retrieval
Julian Killingback, Mahta Rafiee, Madine Manas +1
Dense retrieval, which encodes queries and documents into a single dense vector, has become the dominant neural retrieval approach due to its simplicity and compatibility with fast…
Benchmarking Information Retrieval Models on Complex Retrieval Tasks
Julian Killingback, Hamed Zamani
Large language models (LLMs) are incredible and versatile tools for text-based tasks that have enabled countless, previously unimaginable, applications. Retrieval models, in contra…
Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?
Hansi Zeng, Kai Hui, Honglei Zhuang +4
While metrics available during pre-training, such as perplexity, correlate well with model performance at scaling-laws studies, their predictive capacities at a fixed model size re…
Distillation and Refinement of Reasoning in Small Language Models for Document Re-ranking
Chris Samarinas, Hamed Zamani
We present a novel approach for training small language models for reasoning-intensive document ranking that combines knowledge distillation with reinforcement learning optimizatio…
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Bowen Jin, Hansi Zeng, Zhenrui Yue +5
Efficiently acquiring external knowledge and up-to-date information is essential for effective reasoning and text generation in large language models (LLMs). Prompting advanced LLM…
Scaling Sparse and Dense Retrieval in Decoder-Only LLMs
Hansi Zeng, Julian Killingback, Hamed Zamani
Scaling large language models (LLMs) has shown great potential for improving retrieval model performance; however, previous studies have mainly focused on dense retrieval trained w…
Hypencoder: Hypernetworks for Information Retrieval
Julian Killingback, Hansi Zeng, Hamed Zamani
Existing information retrieval systems are largely constrained by their reliance on vector inner products to assess query-document relevance, which naturally limits the expressiven…
ProCIS: A Benchmark for Proactive Retrieval in Conversations
Chris Samarinas, Hamed Zamani
The field of conversational information seeking, which is rapidly gaining interest in both academia and industry, is changing how we interact with search engines through natural la…
Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models
Chris Samarinas, Pracha Promthaw, Atharva Nijasure +3
This paper explores SynTOD, a new synthetic data generation approach for developing end-to-end Task-Oriented Dialogue (TOD) Systems capable of handling complex tasks such as intent…
Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding
Hansi Zeng, Chen Luo, Hamed Zamani
This paper introduces PAG-a novel optimization and decoding approach that guides autoregressive generation of document identifiers in generative retrieval models through simultaneo…
Scalable and Effective Generative Information Retrieval
Hansi Zeng, Chen Luo, Bowen Jin +3
Recent research has shown that transformer networks can be used as differentiable search indexes by representing each document as a sequences of document ID tokens. These generativ…