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Found 10 papers, 6 papers with code
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Large Multimodal Reasoning Models (LMRMs) have emerged as a promising paradigm, integrating modalities such as text, images, audio, and video to support complex reasoning capabilities and aiming to achieve comprehensive perception, precise understanding, and deep reasoning.
Take Off the Training Wheels Progressive In-Context Learning for Effective Alignment
We find that the transformer embeds the task function learned from demonstrations into the separator token representation, which plays an important role in the generation of prior response tokens.
KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model
As retrieval-augmented generation prevails in large language models, embedding models are becoming increasingly crucial.
RaSeRec: Retrieval-Augmented Sequential Recommendation
Although prevailing supervised and self-supervised learning (SSL)-augmented sequential recommendation (SeRec) models have achieved improved performance with powerful neural network architectures, we argue that they still suffer from two limitations: (1) Preference Drift, where models trained on past data can hardly accommodate evolving user preference; and (2) Implicit Memory, where head patterns dominate parametric learning, making it harder to recall long tails.
Ranked #2 on
Sequential Recommendation
on Amazon-Beauty
SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation
Recent studies in Retrieval-Augmented Generation (RAG) have investigated extracting evidence from retrieved passages to reduce computational costs and enhance the final RAG performance, yet it remains challenging.
Enhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation
In this paper, we propose a model-agnostic framework, named AttrGAU (Attributed Graph Networks with Alignment and Uniformity Constraints), to bring the MIA's superiority into existing attribute-agnostic models, to improve their accuracy and robustness for recommendation.
Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion
Therefore, it is necessary to resort to external knowledge to detect and correct the hallucinated content.
FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG
In this work, we propose a progressive retrieval paradigm with coarse-to-fine granularity for RAG, termed FunnelRAG, so as to balance effectiveness and efficiency.
LLaSA: Large Language and E-Commerce Shopping Assistant
Through instruction tuning on our dataset, the assistant, named LLaSA, demonstrates the potential to function as an omnipotent assistant.
Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching
In short, given a cold-start song request, we expect to retrieve songs with similar audiences and then fastly push the cold-start song to the audiences of the retrieved songs to warm up it.
