LLaVA-Critic: Learning to Evaluate Multimodal Models

• URL: http://arxiv.org/abs/2410.02712v1
• Date: Thu, 3 Oct 2024 17:36:33 GMT
• Title: LLaVA-Critic: Learning to Evaluate Multimodal Models
• Authors: Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan Gu, Heng Huang, Chunyuan Li,
• Abstract summary: We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as a generalist evaluator. LLaVA-Critic is trained using a high-quality critic instruction-following dataset that incorporates diverse evaluation criteria and scenarios.
• Score: 110.06665155812162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. LLaVA-Critic is trained using a high-quality critic instruction-following dataset that incorporates diverse evaluation criteria and scenarios. Our experiments demonstrate the model's effectiveness in two key areas: (1) LMM-as-a-Judge, where LLaVA-Critic provides reliable evaluation scores, performing on par with or surpassing GPT models on multiple evaluation benchmarks; and (2) Preference Learning, where it generates reward signals for preference learning, enhancing model alignment capabilities. This work underscores the potential of open-source LMMs in self-critique and evaluation, setting the stage for future research into scalable, superhuman alignment feedback mechanisms for LMMs.

Related papers

• Unveiling Context-Aware Criteria in Self-Assessing LLMs [28.156979106994537]
  We propose a novel Self-Assessing LLM framework that integrates Context-Aware Criteria (SALC) with dynamic knowledge tailored to each evaluation instance. Empirical evaluations demonstrate that our approach significantly outperforms existing baseline evaluation frameworks. Our method also exhibits a improvement in LC Win-Rate in AlpacaEval2 leaderboard up to a 12% when employed for preference data generation.
  arXiv  Detail & Related papers  (2024-10-28T21:18:49Z)
• LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models [71.8065384742686]
  LMMS-EVAL is a unified and standardized multimodal benchmark framework with over 50 tasks and more than 10 models. LMMS-EVAL LITE is a pruned evaluation toolkit that emphasizes both coverage and efficiency. Multimodal LIVEBENCH utilizes continuously updating news and online forums to assess models' generalization abilities in the wild.
  arXiv  Detail & Related papers  (2024-07-17T17:51:53Z)
• Self-Evaluation of Large Language Model based on Glass-box Features [32.425566330495776]
  The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing on training and prompting strategies. We explore the utility of glass-box features under the scenario of self-evaluation, namely applying an LLM to evaluate its own output.
  arXiv  Detail & Related papers  (2024-03-07T04:50:38Z)
• PRE: A Peer Review Based Large Language Model Evaluator [14.585292530642603]
  Existing paradigms rely on either human annotators or model-based evaluators to evaluate the performance of LLMs. We propose a novel framework that can automatically evaluate LLMs through a peer-review process.
  arXiv  Detail & Related papers  (2024-01-28T12:33:14Z)
• Don't Make Your LLM an Evaluation Benchmark Cheater [142.24553056600627]
  Large language models(LLMs) have greatly advanced the frontiers of artificial intelligence, attaining remarkable improvement in model capacity. To assess the model performance, a typical approach is to construct evaluation benchmarks for measuring the ability level of LLMs. We discuss the potential risk and impact of inappropriately using evaluation benchmarks and misleadingly interpreting the evaluation results.
  arXiv  Detail & Related papers  (2023-11-03T14:59:54Z)
• MMBench: Is Your Multi-modal Model an All-around Player? [114.45702807380415]
  We propose MMBench, a benchmark for assessing the multi-modal capabilities of vision-language models. MMBench is meticulously curated with well-designed quality control schemes. MMBench incorporates multiple-choice questions in both English and Chinese versions.
  arXiv  Detail & Related papers  (2023-07-12T16:23:09Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)
• On Learning to Summarize with Large Language Models as References [101.79795027550959]
  Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets. We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
  arXiv  Detail & Related papers  (2023-05-23T16:56:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.