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Benchmark evaluations, applications, and challenges of large vision language models: A survey
Multimodal Vision Language Models (VLMs) have emerged as a transformative technology
at the intersection of computer vision and natural language processing, enabling machines …
at the intersection of computer vision and natural language processing, enabling machines …
Self-exploring language models: Active preference elicitation for online alignment
Preference optimization, particularly through Reinforcement Learning from Human
Feedback (RLHF), has achieved significant success in aligning Large Language Models …
Feedback (RLHF), has achieved significant success in aligning Large Language Models …
Llava-critic: Learning to evaluate multimodal models
We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as
a generalist evaluator to assess performance across a wide range of multimodal tasks …
a generalist evaluator to assess performance across a wide range of multimodal tasks …
Critic-v: Vlm critics help catch vlm errors in multimodal reasoning
Vision-language models~(VLMs) have shown remarkable advancements in multimodal
reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to …
reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to …
Llm uncertainty quantification through directional entailment graph and claim level response augmentation
The Large language models (LLMs) have showcased superior capabilities in sophisticated
tasks across various domains, stemming from basic question-answer (QA), they are …
tasks across various domains, stemming from basic question-answer (QA), they are …
Teaching-assistant-in-the-loop: Improving knowledge distillation from imperfect teacher models in low-budget scenarios
There is increasing interest in distilling task-specific knowledge from large language models
(LLM) to smaller student models. Nonetheless, LLM distillation presents a dual challenge: 1) …
(LLM) to smaller student models. Nonetheless, LLM distillation presents a dual challenge: 1) …
Explore spurious correlations at the concept level in language models for text classification
Language models (LMs) have achieved notable success in numerous NLP tasks, employing
both fine-tuning and in-context learning (ICL) methods. While language models demonstrate …
both fine-tuning and in-context learning (ICL) methods. While language models demonstrate …
Cream: Consistency regularized self-rewarding language models
Recent self-rewarding large language models (LLM) have successfully applied LLM-as-a-
Judge to iteratively improve the alignment performance without the need of human …
Judge to iteratively improve the alignment performance without the need of human …
Scaling inference-time search with vision value model for improved visual comprehension
Despite significant advancements in vision-language models (VLMs), there lacks effective
approaches to enhance response quality by scaling inference-time computation. This …
approaches to enhance response quality by scaling inference-time computation. This …
Multi-stage balanced distillation: Addressing long-tail challenges in sequence-level knowledge distillation
Large language models (LLMs) have significantly advanced various natural language
processing tasks, but deploying them remains computationally expensive. Knowledge …
processing tasks, but deploying them remains computationally expensive. Knowledge …