cs.IR · 2026-07-14 · No. 53
Information Retrieval, 2026-07-14.
2 new papers in cs.IR. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
2 entries-
01
Enhancing LLMs through human feedback: a journey towards self-improvement
Tatiana Pelc, Gila Kamhi, Asaf Avrahamy, Adi Fledel-Alon
cs.IR · cs.AI · cs.CL
In the rapidly evolving landscape of information retrieval systems, the ability to adapt and improve through user feedback is paramount. This study introduces a novel methodology for refining the performance of a primary Retrieval Augmented Generation (RAG) system by strategically integrating an auxiliary feedback RAG system. By systematically harnessing human-generated feedback, the approach aims to enhance the accuracy, relevance, and...
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02
MMRM: A Multiplex Multimodal Representation Model for Product Ranking in E-commerce Search
Zhen-Lin Chen, Maosen Sheng, Peng Lin, Jianmin Chen, Zhuojian Xiao, Dongyue Wang, Xiwei Zhao
cs.IR · cs.LG · cs.MM
Multimodal information is pivotal for e-commerce search ranking. Existing works leverage multimodal data typically by fine-tuning general Multimodal Large Language Models (MLLMs) via collaborative signals, subsequently integrating the derived representations into ranking models as item features. Despite their efficacy, these methods face two primary limitations: (1) they rely on a single collaborative signal for MLLM fine-tuning, failing to...
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