Process-supervised reward models (PRMs) offer fine-grained, step-wise feedback on model responses, aiding in selecting effective reasoning paths for complex tasks. Unlike output reward models (ORMs), which evaluate responses based on final outputs, PRMs provide detailed assessments at each step, making them particularly valuable for reasoning-intensive applications. While PRMs have been extensively studied in language tasks, their application in multimodal settings remains largely unexplored. Most vision-language reward models still rely on the ORM approach, highlighting the need for further research into how PRMs can enhance multimodal learning and reasoning. Existing reward benchmarks primarily focus on text-based models, with some specifically designed

MarkTechPost

03.04.2025

Nomic Open Sources State-of-the-Art Multimodal Embedding Model - MarkTechPost

Nomic has announced the release of 'Nomic Embed Multimodal,' a groundbreaking embedding model that achieves state-of-the-art performance on visual document retrieval tasks. The new model seamlessly processes interleaved text, images, and screenshots, establishing a new high score on the Vidore-v2 benchmark for visual document retrieval. This advancement is particularly significant for retrieval augmented generation (RAG) applications working with PDF documents, where capturing both visual and textual context is crucial. Breaking New Ground in Visual Document Retrieval The Nomic Embed Multimodal 7B model has achieved an impressive 62.7 NDCG@5 score on the Vidore-v2 benchmark, representing a 2.8-point improvement over previous best-performing

MarkTechPost

03.04.2025

Mitigating Hallucinations in Large Vision-Language Models: A Latent Space Steering Approach - MarkTechPost

Hallucination remains a significant challenge in deploying Large Vision-Language Models (LVLMs), as these models often generate text misaligned with visual inputs. Unlike hallucination in LLMs, which arises from linguistic inconsistencies, LVLMs struggle with cross-modal discrepancies, leading to inaccurate image descriptions or incorrect spatial relationships. These models leverage vision encoders, such as CLIP, alongside pretrained text decoders to map visual information into language. Despite their strong performance in tasks like image captioning, visual question answering, and medical treatment planning, LVLMs remain prone to hallucination, which limits their real-world applicability. The issue stems from various factors, including statistical biases in pretraining, an

MarkTechPost

03.04.2025

Salesforce AI Introduce BingoGuard: An LLM-based Moderation System Designed to Predict both Binary Safety Labels and Severity Levels - MarkTechPost

The advancement of large language models (LLMs) has significantly influenced interactive technologies, presenting both benefits and challenges. One prominent issue arising from these models is their potential to generate harmful content. Traditional moderation systems, typically employing binary classifications (safe vs. unsafe), lack the necessary granularity to distinguish varying levels of harmfulness effectively. This limitation can lead to either excessively restrictive moderation, diminishing user interaction, or inadequate filtering, which could expose users to harmful content. Salesforce AI introduces BingoGuard, an LLM-based moderation system designed to address the inadequacies of binary classification by predicting both binary safety labels and detailed severity levels.

home›MarkTechPost

MarkTechPost

Software

Advancing Vision-Language Reward Models: Challenges, Benchmarks, and the Role of Process-Supervised Learning - MarkTechPost

Nomic Open Sources State-of-the-Art Multimodal Embedding Model - MarkTechPost

Mitigating Hallucinations in Large Vision-Language Models: A Latent Space Steering Approach - MarkTechPost

Salesforce AI Introduce BingoGuard: An LLM-based Moderation System Designed to Predict both Binary Safety Labels and Severity Levels - MarkTechPost

home
›
MarkTechPost