CELLO : Causal Evaluation of Large Vision-Language Models

🌈 Abstract

The paper introduces a fine-grained and unified definition of causality in the vision-language context, going beyond the traditional focus on commonsense causality. Based on this definition, the authors construct a novel dataset called CELLO, which consists of 14,094 causal questions across four levels of causality: discovery, association, intervention, and counterfactual. The paper also proposes CELLO-CoT, a causally inspired chain-of-thought prompting strategy, to effectively elicit causal reasoning in large vision-language models (LVLMs). Extensive experiments on CELLO reveal that current LVLMs still struggle with causal reasoning tasks, but they can benefit significantly from the CELLO-CoT approach.

🙋 Q&A

[01] Introduction

1. What is the key question that the paper aims to address?

• The paper aims to address the question of whether large vision-language models (LVLMs) really understand causality, as their recent advancements have promoted a surge of research in vision-language tasks.

2. What are the limitations of previous work on causal reasoning in vision-language tasks?

• Previous work has primarily focused on commonsense causality between events and/or actions, neglecting the fine-grained causal relationships between humans and objects, between humans, and between objects. This limits the effectiveness of decision-making in real-world environments, such as embodied intelligent agents and autonomous driving systems.
• Previous studies typically do not explicitly define the underlying causal graphs for key entities, rendering it challenging to systematically investigate the formal causal reasoning ability of LVLMs.

3. What are the main contributions of this paper?

• The paper introduces a fine-grained and unified definition of causality in the vision-language context.
• The authors construct CELLO, a novel dataset designed to rigorously evaluate the causal reasoning abilities of LVLMs, consisting of 14,094 causal questions spanning all four causal levels.
• The paper proposes CELLO-CoT, a causally inspired chain-of-thought prompting strategy, to effectively elicit the causal reasoning in LVLMs.
• The authors conduct extensive experiments on ten leading LVLMs to assess their performance on causal reasoning tasks, identifying their specific limitations and providing valuable insights for future research.

[02] Causality in Vision-Language Context

1. What are the three distinct categories of causal relations in a scene identified in the paper?

• Object-object causal relation: Interactions between objects, such as "the stick holding the balloon."
• Human-object causal relation: Interactions between humans and objects, such as "the woman and child holding the stick."
• Human-human causal relation: Interactions between humans, such as "the woman holding the child."

2. How do these causal relations facilitate the understanding of complex scenes?

• Understanding these causal relations helps in comprehending physical interactions and dependencies within a scene, human actions and their interactions with the surrounding environment, as well as social interactions and human behaviors.
• This facilitates more precise and significant interpretations of complex scenes, which is crucial for applications like embodied artificial intelligence and autonomous driving systems.

[03] The CELLO Dataset

1. What are the three main steps in the CELLO dataset construction process?

• Causal graph extraction: Constructing causal graphs based on the relationships described between entities in the Visual Genome dataset.
• Causal task selection: Selecting representative causal tasks of the ladder of causation from previous literature.
• Causal question construction: Designing templates for each task type and using an LLM to generate causal questions.

2. What are the four levels of causality covered in the CELLO dataset?

• Discovery: Identifying cause-effect pairs from observational data.
• Association: Identifying potential dependencies between variables.
• Intervention: Exploring the effects of manipulating certain variables.
• Counterfactual: Considering hypothetical scenarios to understand what could have happened under different conditions.

3. How are the multiple-choice answers constructed for the CELLO dataset?

• The correct answer is derived by applying causal reasoning rules.
• The three distractors are constructed using: 1) Irrelevant entities, 2) Partially correct entities, and 3) Induced entities.

[04] The CELLO-CoT Strategy

1. What are the four steps in the CELLO-CoT prompting strategy?

• Extracting core entities from the question text.
• Identifying the causal graph structure represented in the image.
• Determining the type of causal task.
• Compiling knowledge of causal inference relevant to the current task.

2. How does the CELLO-CoT strategy help LVLMs tackle causal reasoning problems?

• The CELLO-CoT strategy imposes an inductive bias on LVLMs, providing a structured approach to analyze causal questions step-by-step, enabling more effective problem-solving.

[05] Experiments

1. What are the key findings from the experiments on the CELLO dataset?

• Existing LVLMs perform poorly on causal reasoning tasks, with some models even underperforming random guessing.
• There is notable variability in how different models perform across various types of causal reasoning tasks, reflecting distinct strengths and weaknesses of each model.
• The CELLO-CoT strategy significantly enhances the performance of LVLMs on causal tasks.
• LVLMs' understanding of causal relationships is vulnerable, as shown by the significant drop in performance during robustness testing.

2. How do the results provide insights for future research?

• The analysis identifies the specific limitations of current LVLMs in causal reasoning, which can guide future research to address these shortcomings.
• The effectiveness of the CELLO-CoT strategy suggests that incorporating structured causal reasoning approaches can be a promising direction for improving LVLMs' causal understanding.

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