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Found 8 papers, 6 papers with code
RAR-b: Reasoning as Retrieval Benchmark
Under the emerging Retrieval-augmented Generation (RAG) paradigm, we envision the need to evaluate next-level language understanding abilities of embedding models, and take a conscious look at the reasoning abilities stored in them.
Pixel Sentence Representation Learning
To our knowledge, this is the first representation learning method devoid of traditional language models for understanding sentence and document semantics, marking a stride closer to human-like textual comprehension.
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval
We further annotate the image-text pairs with two-level subset-subcategory hierarchy annotations to facilitate a more comprehensive evaluation of the baselines.
Length is a Curse and a Blessing for Document-level Semantics
In recent years, contrastive learning (CL) has been extensively utilized to recover sentence and document-level encoding capability from pre-trained language models.
Effective Distillation of Table-based Reasoning Ability from LLMs
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks.
Audio Contrastive based Fine-tuning
Audio classification plays a crucial role in speech and sound processing tasks with a wide range of applications.
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored.
On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation Learning
In this paper, we aim to help guide future designs of sentence representation learning methods by taking a closer look at contrastive SRL through the lens of isotropy, contextualization and learning dynamics.
