Fangzheng Tian
@DanielTian97
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A PhD student at University of Glasgow. Working on Information Retrieval and Natural Language Processing.
Glasgow, Scotland
Joined November 2021
Cheers to 15 years of wonderful memories. Join us one last time, Downton Abbey: The Grand Finale is in theaters TODAY. 🎟️: https://t.co/8AnYwIeZET
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📢 Call For Papers: 1st workshop on Proactive Conversational Information Seeking with Large Language Models @cikm2025 For more info visit: https://t.co/IuPpmNNWCK 📅 When: November 14, 2025 📝 Deadline: August 31, 2025 #ProActLLM #cikm2025 #LLM #ProactiveAI #ConversationalAI
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Journal article “Unifying Isolated Processes for Enhanced Multi-modal Recommendations Using a Graph Transformer” has been accepted at ACM Transactions on Recommender Systems (ToRS); Joint work with @ZixuanYI_  & part of a special issue on best papers from ACM #RecSys 2024
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Want your LLMs to be safe and responsibly aligned? Time to vaccinate them! 🧬 Fascinating #ICDCS2025 keynote by Ling Liu (Georgia Tech) on reducing fine-tuning costs while boosting safety in LLM deployment.
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The welcome reception at #ICDCS2025 is buzzing with energy! Fantastic to see so many smiling faces and lively conversations — a vibrant start to the conference. Here's to a great main conference ahead! 🎉
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Delighted to see my former PhD student at @ir_glasgow and @GlasgowCS Jingmin Huang, now a lecturer at the University of Southampton; Jingmin served as Demo Co-Chair at #ICDCS2025. Great to catch up at the conference!
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After attending the panel at the Explainability for IR workshop, now attending my student's talk at the IR4RAG workshop. This paper is a first step towards building an adaptive multi-agent RAG pipeline. @craig_macdonald @DanielTian97
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Now time for @ir_glasgow 's @DanielTian97 to present his impeccable QPP work "Am I on the Right Track?" at the IR-RAG Workshop #SIGIR2025 (work w/t @JinyuanF, @debforit, @mengzaiqiao and @craig_macdonald)
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.@DanielTian97 is presenting our work on using QPP in agentic RAG w/ @JinyuanF @debforit @mengzaiqiao 📄 https://t.co/OkVsq7hQ0a
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How does retrieval impact on Agentic RAG? Let's see what predicted intermediate retrieval quality can tell us! Our IR-RAG@SIGIR'25 paper is on Arxiv: https://t.co/FPBEFQbuu5.
#SIGIR2025 cc/ @JinyuanF @debforit
@mengzaiqiao @craig_macdonald
arxiv.org
Agentic Retrieval-Augmented Generation (RAG) is a new paradigm where the reasoning model decides when to invoke a retriever (as a "tool") when answering a question. This paradigm, exemplified by...
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Am I on the Right Track? What Can Predicted Query Performance Tell Us about the Search Behaviour of Agentic RAG @DanielTian97 et al. examine how query performance prediction can provide insights into the search behavior of agentic RAG systems. 📝 https://t.co/G2SncsKT4L
arxiv.org
Agentic Retrieval-Augmented Generation (RAG) is a new paradigm where the reasoning model decides when to invoke a retriever (as a "tool") when answering a question. This paradigm, exemplified by...
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Today, Florin Cuconasu @FlorinCuconasu from the Sapienza University of Rome @SapienzaRoma is giving an #IRTalk entitled "Beyond Relevance: Understanding the Distracting Effect of Retrieved Passages in RAG Systems". Details: https://t.co/oELqLtDZL4
@GlasgowCS
@ir_glasgow
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At 15:00 on 7th July, Florin Cuconasu from Sapienza University of Rome will give an #IRTalk entitled "Beyond Relevance: Understanding the Distracting Effect of Retrieved Passages in RAG Systems". Details: https://t.co/x3krgiA4Zs
@GlasgowCS
@ir_glasgow
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❓Which retrieval library should you use for RAG? Which agent library? Where do you get datasets? We made PyTerrier RAG to stop your headaches! Led by @craig_macdonald /w (@JinyuanF , me and @mengzaiqiao )! 🧵 ⬇️
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🚨 New Pre-Print! We show that in-context learning can steer LLM rankers to satisfy multiple objectives at once, relevance and fairness/diversity, without any parameter updates. Work done jointly with @nilanjansb from IIT Kharagpur and @debforit. 🧵 Below!
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🚨 New Pre-Print! You've just added your 600th model to your negative mining pool and filtered all false negatives. Does any of this even matter when we can apply distillation? In this work with @debforit and @macavaney, we explore data selection in modern ranking. 🧵 Below
Disentangling Locality and Entropy in Ranking Distillation @MrParryParry et al. separate example selection effects from teacher ranking entropy in neural ranking model optimization, showing complex hard-negative pipelines offer minimal gains. 📝 https://t.co/7dy5y2hTRj
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Today, Debasis Ganguly @debforit from the University of Glasgow is giving an #IRTalk entitled "The Role of Query Performance Prediction in Developing Adaptive Search and RAG Systems". Details: https://t.co/MOnTkHXiK8
@GlasgowCS
@ir_glasgow
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Finishing up #ECIR2025 for @ir_glasgow is @andreas_chari talking about his work (w/ @macavaney & @iadh) on improving low-resource retrieval with linguistic similarity transfer! A great end to the conference! @ir_glasgow @glasgowcs
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Delighted to deliver the keynote at the QPP++ workshop @ecir2025. Shared my view on the role of QPP models for Adaptive IR and RAG systems - building on our last year's SIGIR perspective https://t.co/7GRLR4ZNUj w/ @MrParryParry & Manish Chandra. Slides:
Back to back speakers from Glasgow in the QPP++ workshop at #ECIR2025, with lots of other interesting talks coming up. @DanielTian97 is presenting his work (w/ @craig_macdonald & @debforit) that revisits query variants for QPP. Great stuff! @ir_glasgow @glasgowcs #ECIR2025
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Happy to share some of the ongoing work of @DanielTian97 w/ @craig_macdonald as a vision for the potential of applying #qpp for adaptive #rag. For those who missed the talk, here're the slides: https://t.co/MkCriG44ly
#ecir2025 @ir_glasgow
@debforit is giving keynote talk about QPP’s application in LLM era at the QPP++ workshop #ecir2025 @ir_glasgow
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