Title: The Instinctive Bias: Spurious Images lead to Illusion in MLLMs
Benchmark: CorrelationQA
Summary: This paper examines the extent to which multimodal LMs are distracted by spurious information provided via images in the context of commonsense question answering, which the authors refer to as "instinctive bias." The authors create CorrelationQA, a benchmark consisting of (i) commonsense questions, (ii) answer choices, and (iii) images in various forms that either support the correct or one of the incorrect answer choices. Applying CorrelationQA to a series of mulitmodal LMs, the authors find that all of them struggle if the information in the image does not support the correct answer choice.
Phenomenon type: Specific form of bias
Target phenomenon: They want to measure "instinctive bias" in multimodal LMs, i.e., the tendency to pick an incorrect answer choice in commonsense question answering if that choice is supported by an image provided in the prompt.
Phenomenon defined by authors: Yes
Phenomenon definition: "ignor[ing] the semantic information in reasoning quizzes and answer[ing] directly to the objects in the pictures instead of utilizing their reasoning ability" (p. 16163)
Definition scope: Subset
Definition integrity: Single cohesive phenomenon
Definition integrity detail: Not applicable
Phenomenon contested: Widely-agreed
Task definition: The task is defined as answering commonsense questions (multiple choice) in the presence of an image that either depicts the correct or one of the incorrect answer choices.
Task item definition: Each item consists of (i) a commonsense question, (ii) one correct and five incorrect answer choices, and (iii) an image depicting one of the six answer choices.
Task definition detail: Each question occurs six times in the benchmark (with an image for each of the six answer options).
Task source: Procedurally-generated task examples (e.g. Creating instances from a template), LLM-generated task examples (e.g. Filtered from responses to a prompt)
Task source detail: LMs are used for all steps: creating the questions, answer choices, and images.
Task ecology: Constructed task (e.g. predicting medical diagnoses from clinicians' notes)
Dataset size: 7,308
Dataset metadata available: No
Sampling method: Targeted items (creators defined a task space and chose tasks within it strategically)
Split reviewed: Test
Response format: Multiple choice
Metric definition: Exact Match (accuracy, F1, precision, recall), They also report accuracy drop between cases where the image supports the correct answer choice and the cases where it supports one of the incorrect answer choices.
Metric aggregation: Simple Mean
Metric statistics: simple mean
Metric subscores: Yes
Metric subscores detail: 13 subsets based on the class of the entity the question is about: animal, art, color, city, food, history, human, material, natural, objects, plant, sports, technology
Metric access: Outputs alone
Few-shot evaluation: No
Authorship: Mix (multiple authors from industry and academia)
Benchmark availability: Yes
Task face validity: Yes
Metric face validity: Yes
Results interpreted by authors: Yes
Results compared: No
Results comparison explanation: No comparisons made
Results realism: No
Human baseline: No
Authors discuss validity: No
Taxonomy: Alignment / Bias
New taxonomy: General Capability / Bias