7 citations · 23 across the 30 of their papers we have counts for
31 papers
Critic-R: Improving Agentic Search using Instruction-tuned Retrievers with Natural Language Introspective Feedback
Md Zarif Ul Alam, Alireza Salemi, Hamed Zamani
Agentic search systems iteratively interact with retrieval models to answer complex queries. Despite substantial progress, optimizing retrievers for agentic search remains challeng…
GrepSeek: Training Search Agents for Direct Corpus Interaction
Alireza Salemi, Chang Zeng, Atharva Nijasure +4
Large Language Model (LLM) search agents have shown strong promise on knowledge-intensive tasks through iterative reasoning and retrieval. Most existing systems rely on retrievers…
Evaluation of Agents under Simulated AI Marketplace Dynamics
To Eun Kim, Alireza Salemi, Hamed Zamani +1
Modern information access ecosystems consist of mixtures of systems, such as retrieval systems and large language models, and increasingly rely on marketplaces to mediate access to…
Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering
Maryam Amirizaniani, Alireza Salemi, Hamed Zamani
Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context. Existing state-of-the-ar…
Improving User Privacy in Personalized Generation: Client-Side Retrieval-Augmented Modification of Server-Side Generated Speculations
Alireza Salemi, Hamed Zamani
Personalization is crucial for aligning Large Language Model (LLM) outputs with individual user preferences and background knowledge. State-of-the-art solutions are based on retrie…
Beyond a Million Tokens: Benchmarking and Enhancing Long-Term Memory in LLMs
Mohammad Tavakoli, Alireza Salemi, Carrie Ye +3
Evaluating the abilities of large language models (LLMs) for tasks that require long-term memory and thus long-context reasoning, for example in conversational settings, is hampere…
LLM-Based Multi-Agent Blackboard System for Information Discovery in Data Science
Alireza Salemi, Mihir Parmar, Palash Goyal +5
Advances in large language models (LLMs) have created new opportunities in data science, but their deployment is often limited by the challenge of finding relevant data in large da…
Pathways of Thoughts: Multi-Directional Thinking for Long-form Personalized Question Answering
Alireza Salemi, Cheng Li, Mingyang Zhang +7
Personalization is well studied in search and recommendation, but personalized question answering remains underexplored due to challenges in inferring preferences from long, noisy,…
Learning from Natural Language Feedback for Personalized Question Answering
Alireza Salemi, Hamed Zamani
Personalization is crucial for enhancing both the effectiveness and user satisfaction of language technologies, particularly in information-seeking tasks like question answering. C…
CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-Training
Alireza Salemi, Mukta Maddipatla, Hamed Zamani
This paper presents mRAG, a multi-agent retrieval-augmented generation (RAG) framework composed of specialized agents for subtasks such as planning, searching, reasoning, and coord…
LaMP-QA: A Benchmark for Personalized Long-form Question Answering
Alireza Salemi, Hamed Zamani
Personalization is essential for question answering systems that are user-centric. Despite its importance, personalization in answer generation has been relatively underexplored. T…
Plan-and-Refine: Diverse and Comprehensive Retrieval-Augmented Generation
Alireza Salemi, Chris Samarinas, Hamed Zamani
This paper studies the limitations of (retrieval-augmented) large language models (LLMs) in generating diverse and comprehensive responses, and introduces the Plan-and-Refine (P&R)…
Personalized Generation In Large Model Era: A Survey
Yiyan Xu, Jinghao Zhang, Alireza Salemi +6
In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents t…
Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation
Alireza Salemi, Cheng Li, Mingyang Zhang +6
Personalized text generation requires a unique ability of large language models (LLMs) to learn from context that they often do not encounter during their standard training. One wa…
ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generation
Alireza Salemi, Julian Killingback, Hamed Zamani
Evaluating personalized text generated by large language models (LLMs) is challenging, as only the LLM user, i.e., prompt author, can reliably assess the output, but re-engaging th…
Beyond Factual Accuracy: Evaluating Coverage of Diverse Factual Information in Long-form Text Generation
Chris Samarinas, Alexander Krubner, Alireza Salemi +2
This paper presents ICAT, an evaluation framework for measuring coverage of diverse factual information in long-form text generation. ICAT breaks down a long output text into a lis…
Accelerating Retrieval-Augmented Generation
Derrick Quinn, Mohammad Nouri, Neel Patel +5
An evolving solution to address hallucination and enhance accuracy in large language models (LLMs) is Retrieval-Augmented Generation (RAG), which involves augmenting LLMs with info…
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization
Alireza Salemi, Hamed Zamani
This paper investigates the design of a unified search engine to serve multiple retrieval-augmented generation (RAG) agents, each with a distinct task, backbone large language mode…
Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models
Alireza Salemi, Hamed Zamani
Despite its substantial impact on various search, recommendation, and question answering tasks, privacy-preserving methods for personalizing large language models (LLMs) have recei…
Retrieval-Enhanced Machine Learning: Synthesis and Opportunities
To Eun Kim, Alireza Salemi, Andrew Drozdov +2
In the field of language modeling, models augmented with retrieval components have emerged as a promising solution to address several challenges faced in the natural language proce…
LongLaMP: A Benchmark for Personalized Long-form Text Generation
Ishita Kumar, Snigdha Viswanathan, Sushrita Yerra +11
Long-text generation is seemingly ubiquitous in real-world applications of large language models such as generating an email or writing a review. Despite the fundamental importance…
Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models
Alireza Salemi, Hamed Zamani
This paper introduces uRAG--a framework with a unified retrieval engine that serves multiple downstream retrieval-augmented generation (RAG) systems. Each RAG system consumes the r…
Evaluating Retrieval Quality in Retrieval-Augmented Generation
Alireza Salemi, Hamed Zamani
Evaluating retrieval-augmented generation (RAG) presents challenges, particularly for retrieval models within these systems. Traditional end-to-end evaluation methods are computati…
Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation
Alireza Salemi, Surya Kallumadi, Hamed Zamani
This paper studies retrieval-augmented approaches for personalizing large language models (LLMs), which potentially have a substantial impact on various applications and domains. W…