Search Results for author:
Found 6 papers, 5 papers with code
How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?
Vision-Language adaptation (VL adaptation) transforms Large Language Models (LLMs) into Large Vision-Language Models (LVLMs) for multimodal tasks, but this process often compromises the inherent safety capabilities embedded in the original LLMs.
Hierarchical Deconstruction of LLM Reasoning: A Graph-Based Framework for Analyzing Knowledge Utilization
Based on a hierarchical graph, we quantify forward discrepancy, a discrepancy in LLM performance on simpler sub-problems versus complex questions.
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
To overcome these limitations, we introduce the BiGGen Bench, a principled generation benchmark designed to thoroughly evaluate nine distinct capabilities of LMs across 77 diverse tasks.
Aligning to Thousands of Preferences via System Message Generalization
Using this dataset, we train a 7B LLM called Janus and test it on 921 prompts from 5 benchmarks (AlpacaEval 2. 0, FLASK, Koala, MT-Bench, and Self-Instruct) by adding various unseen system messages that reflect user preferences.
Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation
Assessing long-form responses generated by Vision-Language Models (VLMs) is challenging.
Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision
Building on this approach, we introduce Volcano, a multimodal self-feedback guided revision model.
Ranked #76 on
Visual Question Answering
on MM-Vet
