contrastive-learning

This paper presents PTEI, a framework that integrates personality traits (MBTI and OCEAN) into LLMs to enhance emotional intelligence, using contrastive learning and personality-aware prompts. Experiments show significant improvements in emotional understanding, especially when combined with Chain-of-Thought reasoning.

This paper proposes output-aware safety guardrails for multimodal large language models that use hidden state representations and multi-instance contrastive learning to predict unsafe outputs before generation, drastically reducing over-refusal while maintaining safety. The method preserves the model's utility by intervening only when the actual response would be harmful.

VTaMo introduces explicit multi-granularity video-text alignment for sign language translation using optimal transport and contrastive learning, achieving state-of-the-art performance on four benchmarks.

ConOrd proposes a contrastive learning framework for ordinal regression that integrates contrastive learning and order learning, achieving state-of-the-art performance on facial age estimation, image quality assessment, and video quality assessment.

COALA is a robust framework for contextual biasing in automatic speech recognition (ASR) that uses a contrastive regularizer and biasing score estimation to improve recognition of domain-specific entities from large biasing lists. Experiments on LibriSpeech show consistent superior performance.

Omni-Sleep is a sleep foundation model that uses hierarchical contrastive learning to capture CNS-ANS dynamics from multimodal polysomnography signals, outperforming strong baselines on sleep staging and multi-disease classification.

This paper analytically computes the optimal representations under a contrastive loss for basic augmentations and natural images with stationary statistics, showing that the optimal CNN first-layer filters are sinusoids and that weights can be computed via a waterfilling algorithm.

A researcher reports a surprising 50-point accuracy gap between frozen SigLIP2 (92%) and DINOv2 (41%) embeddings on a fine-grained car classification task using k-NN, seeking insight on whether a linear probe would close the gap or if DINOv2 is unsuited for retrieval.

This paper proposes SCISE, a scalable unsupervised graph clustering framework that uses community-aware sampling and structural entropy to overcome structural isolation in mini-batch training, achieving state-of-the-art results on benchmark datasets.

MABLE combines masked reconstruction with cosine-similarity losses to learn node and graph embeddings from large heterogeneous graphs, demonstrated on geospatial mineral-exploration data. It unifies masked autoencoding and metric learning in a self-supervised framework without requiring labeled data.

KARMA proposes a knowledge graph-based approach to generate slot-aligned contrastive candidates and uses Slot-Parallel Alignment (SPA) to apply preference optimization at the entity-slot level, addressing the Resolution Mismatch Problem in LLM reasoning supervision.

SPARCLE is a speaker-aware grapheme representation model that uses contrastive learning to align grapheme embeddings with acoustic representations, improving text-to-speech quality especially in low-resource settings.

PixCon proposes a clean-positive pixel-contrastive framework for semi-supervised semantic segmentation that guarantees contamination-free positive sets via per-class memory banks, improving accuracy over existing methods on benchmarks like Pascal VOC, Cityscapes, and ADE20K.

Proposes scKDGM, a framework that uses KAN-guided dynamic graph masked learning and cross-view contrastive learning for clustering single-cell RNA-seq data, achieving state-of-the-art performance on 12 real datasets.

This paper proposes Rank-Aware Hyperbolic Alignment (RAHA), a method for vision-language dataset distillation that leverages hyperbolic geometry and alignment capacity control to efficiently compress large image-text datasets into high-quality synthetic pairs.

This paper derives a scaling law for sketched linear contrastive learning under a Gaussian latent-variable model, analyzing how risk decomposes into approximation, optimization, and statistical terms, and provides theoretical guidance for balancing model size, data, and compute in contrastive learning.

This paper introduces BitEmbed, an extreme low-bit framework for LLM-based text embeddings that converts pretrained LLM backbones into BitNet-style encoders with ternary weights and quantized activations. It achieves comparable performance to full-precision models while significantly reducing encoding and storage costs.

This paper introduces three datasets (Hell-Char, PaLit-Char, Med-Char) for diachronic representation learning of ancient Greek letterforms and proposes a similarity-weighted supervised contrastive loss with lacuna-driven augmentation to robustly learn character embeddings across centuries of handwriting variation.

This paper introduces the MELD dataset for evaluating whether text embedding models capture mathematical equivalence across different terminologies, and finds that current models fail. It proposes a contrastive learning approach to align informal and formal mathematical statements, improving retrieval on both informal-formal and natural language tasks.

V-Zero is a novel label-free framework for fine-grained visual reasoning that uses contrastive evidence gating and on-policy distillation to improve performance without annotated answer labels, achieving faster training than traditional methods.