19 citations · 36 across the 15 of their papers we have counts for
8 papers · 1 filter
Brain-Language Model Alignment: Insights into the Platonic Hypothesis and Intermediate-Layer Advantage
Ángela López-Cardona, Sebastián Idesis, Mireia Masias-Bruns +2
Do brains and language models converge toward the same internal representations of the world? Recent years have seen a rise in studies of neural activations and model alignment. In…
Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories
Georgios Koutroumpas, Sebastian Idesis, Mireia Masias Bruns +4
Traditionally, Recommender Systems (RS) have primarily measured performance based on the accuracy and relevance of their recommendations. However, this algorithmic-centric approach…
The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Junchen Fu, Xuri Ge, Xin Xin +5
Multimodal representation learning has garnered significant attention in the AI community, largely due to the success of large pre-trained multimodal foundation models like LLaMA,…
Integrating Cognitive Processing Signals into Language Models: A Review of Advances, Applications and Future Directions
Angela Lopez-Cardona, Sebastian Idesis, Ioannis Arapakis
Recently, the integration of cognitive neuroscience in Natural Language Processing (NLP) has gained significant attention. This article provides a critical and timely overview of r…
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation
Junchen Fu, Yongxin Ni, Joemon M. Jose +4
In this paper, we explore a less-studied yet practically important problem: how to efficiently and effectively adapt multiple (2) multimodal foundation models (MFMs) for the seq…
OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses
Angela Lopez-Cardona, Sebastian Idesis, Miguel Barreda-Ángeles +2
While Large Language Models (LLMs) have significantly advanced natural language processing, aligning them with human preferences remains an open challenge. Although current alignme…
LLMPopcorn: Exploring LLMs as Assistants for Popular Micro-video Generation
Junchen Fu, Xuri Ge, Kaiwen Zheng +5
In an era where micro-videos dominate platforms like TikTok and YouTube, AI-generated content is nearing cinematic quality. The next frontier is using large language models (LLMs)…
Large Language Model driven Policy Exploration for Recommender Systems
Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis +1
Recent advancements in Recommender Systems (RS) have incorporated Reinforcement Learning (RL), framing the recommendation as a Markov Decision Process (MDP). However, offline RL po…