LLaVA Architecture › Evaluating Multimodal Models

Before discussing LLaVA's limitations, it is useful to understand how multimodal models are evaluated, because evaluation criteria shape what we consider a "good" or "bad" model and what failures are most consequential in practice.

Multimodal evaluation draws from several families of benchmarks, each testing different aspects of visual understanding:

Visual Question Answering (VQA): Datasets like VQA-v2 and GQA present images with multiple-choice or open-ended questions. VQA-v2 was designed to reduce language bias so that questions have balanced answer distributions (roughly half yes, half no for yes/no questions), forcing models to use visual information rather than relying on linguistic priors. GQA focuses on compositional reasoning, with questions that require combining multiple visual facts (for example, "Is the item to the left of the bench a chair?").

ScienceQA tests multimodal scientific reasoning, presenting diagrams and charts alongside domain-specific images and questions that require integrating visual and textual knowledge. This benchmark is particularly demanding because the visual content is often abstract (diagrams rather than photographs) and the questions require understanding scientific concepts, not just object recognition.

MMBench and MME are broader evaluation suites designed to assess a wide range of visual capabilities including object existence, counting, position, color recognition, OCR, commonsense reasoning in a standardized format. These complete suites were developed because narrow benchmarks like VQA-v2 could be "solved" by models that were weak on other visual tasks.

TextVQA and OCR-VQA specifically test text recognition and reasoning over images containing text. These benchmarks are particularly sensitive to resolution: at 224px, small text is often illegible to the vision encoder, and model performance on these benchmarks improved substantially when LLaVA 1.5 introduced higher-resolution processing.

Numerical benchmarks capture some aspects of visual understanding but miss others, particularly conversational quality, instruction following, and the ability to handle open-ended queries that do not have a single correct answer. The original LLaVA paper introduced LLaVA-Bench, a set of 60 diverse in-the-wild images paired with 3 types of instructions: conversational, detailed description, and complex reasoning. Responses are evaluated by GPT-4V on a scale of 1-10, creating a relative score that compares the evaluated model to a reference model.

This LLM-as-judge evaluation paradigm, where a powerful model scores free-form responses, has become increasingly popular in the field because it captures aspects of response quality that automatic metrics (like BLEU or CIDEr) miss: factual accuracy and response depth as well as coherence and relevance to the specific visual content. However, this approach has its own biases, including a tendency for the judge model to prefer longer responses regardless of their accuracy and to favor outputs that stylistically resemble the judge's own generation patterns.

Object hallucination has received its own dedicated evaluation methodology. The POPE (Polling-based Object Probing Evaluation) benchmark asks adversarial yes/no questions about whether specific objects are present in an image, using objects that are absent but semantically plausible given the scene context. A model with strong visual grounding will answer "no" correctly; a model that hallucinates will confirm the presence of objects that were never in the image.

LLaVA models show moderate hallucination rates on POPE, performing better than early baselines but lagging behind models specifically trained with anti-hallucination objectives. This is an active area of improvement, and later models in the LLaVA lineage incorporate dedicated training steps to reduce hallucination, including reinforcement learning from human feedback and contrastive visual grounding.