7 citations · 23 across the 30 of their papers we have counts for
24 papers · 1 filter
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…
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…
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)…
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…
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…
PersianMind: A Cross-Lingual Persian-English Large Language Model
Pedram Rostami, Ali Salemi, Mohammad Javad Dousti
Large language models demonstrate remarkable proficiency in various linguistic tasks and have extensive knowledge across various domains. Although they perform best in English, the…
PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document Generation
Alireza Salemi, Amirhossein Abaskohi, Sara Tavakoli +2
Multilingual pre-training significantly improves many multilingual NLP tasks, including machine translation. Most existing methods are based on some variants of masked language mod…
LaMP: When Large Language Models Meet Personalization
Alireza Salemi, Sheshera Mysore, Michael Bendersky +1
This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark -- a novel benchmark for training and evaluating language models…
ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization
Alireza Salemi, Emad Kebriaei, Ghazal Neisi Minaei +1
Abstractive text summarization is one of the areas influenced by the emergence of pre-trained language models. Current pre-training works in abstractive summarization give more poi…
UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models
Alireza Salemi, Nazanin Sabri, Emad Kebriaei +2
Detecting which parts of a sentence contribute to that sentence's toxicity -- rather than providing a sentence-level verdict of hatefulness -- would increase the interpretability o…