🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Thanks L3S Research Center @L3S_Research_Center@wisskomm for organizing the #L3Sretreat in #Goslar, it was wonderful evening with colleagues and #German 🍺

So glad to win this prize for my master thesis! Thanks for your help and support Lijun Lyu and Avishek Anand, it is a good start of this wonderful journey! Cheers☺️

Explainable Information Retrieval: A Survey 🔗: arxiv.org/abs/2211.02405

Happy to share our survey about explainable IR, comments are appreciated. Avishek Anand Max Idahl Jonas Wallat Yumeng Wang Joshua Ghost

📢 New research alert! 💡 We've developed PRODIGY: pretraining framework for in-context learning over graphs. Through a novel prompt graph representation and a family of in-context pretraining objectives, our model can adapt to novel tasks on unseen graphs 📈. Outperforming

Reminder! #DIR2023 is almost upon us 😉 Interested in presenting your published work, emerging research direction, & even resources of interest to #IR community during special sessions? Submit the contribution form by October 14. More details: dir2023.github.io/DIR2023/

Thrilled to announce that our paper on 'Deep Reinforcement Learning Approach to Configuration Sampling Problem' with my PhD student Amir Abolfazli has won the Best Paper Award at #ICDM2023! 🌟 Kudos also to Jakob Spiegelberg, and Greg Palmer L3S Research Center @L3S_Research_Center@wisskomm TU Delft | AI Initiative TU Delft

Mandeep Rathee is presenting his work on "Bagel: A Benchmark for Assessing Graph Neural Network Explanation" at #AIMLAI (@ECML/PKDD 2024), work in collaboration with Thorben Funke, Avishek Anand and Megha Khosla

Guiding Retrieval using LLM-based Listwise Rankers Introduces a method to integrate listwise LLM rerankers into adaptive retrieval, improving nDCG@10 by up to 13.23% and recall by 28.02% while maintaining efficiency. 📝arxiv.org/abs/2501.09186 👨🏽‍💻github.com/Mandeep-Rathee…

At 15:00 on 3rd March, Mandeep Rathee Mandeep Rathee from the L3S Research Center L3S Research Center @L3S_Research_Center@wisskomm will give an #IRTalk entitled "Beyond Bounded Recall: Adaptive Retrieval using Query Affinity Modelling and LLM based Rerankers". Details: samoa.dcs.gla.ac.uk/events/viewtal… UofG Computing Science Glasgow IR Group

It was a great experience to present my work at the Glasgow IR group Glasgow IR Group. This talk was based on recently accepted papers -Quam (accepted at #WSDM2025) and SlideGAR (accepted at #ECIR2025) --done in collaboration with Sean MacAvaney Sean MacAvaney and Avishek Anand Avishek Anand

At 15:00 on 17th March, Venktesh Viswanathan from TU Delft TU Delft will give an #IRTalk entitled "Bridging Retrieval and Reasoning Gaps for Complex Information Needs". Details: samoa.dcs.gla.ac.uk/events/viewtal… UofG Computing Science Glasgow IR Group

SUNAR: Semantic Uncertainty based Neighborhood Aware Retrieval for Complex QA Leverages LLMs to guide Neighborhood Aware Retrieval, dynamically exploring document neighborhoods based on semantic uncertainty. 📝arxiv.org/abs/2503.17990 👨🏽‍💻github.com/VenkteshV/SUNAR

I’m presenting my paper "Guiding Retrieval using LLM-based Listwise Rankers" at #ECIR2025 this Wednesday! This work is done with Sean MacAvaney and Avishek Anand We explore how LLMs can guide listwise ranking to improve search effectiveness. 📄 Paper: arxiv.org/pdf/2501.09186

About rankers and rerankers, now at #ECIR2025 in San Francesco Church, Scuola IMT Alti Studi Lucca

After a great banquet last night, now we're enjoying a great presentation from Mandeep Rathee about Guiding Retrieval using Large Language Models! Glasgow IR Group @glasgow_cs #ECIR2025

Had a great time at #ECIR2025! 🎉 in Lucca, Italy. I met some amazing researchers. Really enjoyed the company for my all collaborators venktesh , Sean MacAvaney and Avishek Anand. Thanks Vinay Setty for suggesting amazing food places in the city.

🎉 Thrilled to share that I will presenting our paper SUNAR at #NAACL2025! on May 6, 2025 We introduce a novel approach that leverages LLMs to guide neighborhood-aware retrieval for complex QA. This work is done with venktesh and Avishek Anand Full paper: arxiv.org/pdf/2503.17990