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What Does the Model See? Representation Validity in Multimodal LLM-Based Video Analysis

Paper recorded by Signals 4 on 2026-11-03 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-03 on arXiv · recorded by Signals 4 on 2026-11-04

Category: cs.CL · 自然语言处理 · first seen 2026-11-04

Abstract

Multimodal large language models (MLLMs) allow social scientists to analyze video at scale, but researchers must decide which parts of a video to provide as input. We use the term representation validity to examine how text, audio, video, and combined inputs affect measurement and subsequent analysis. We compare input representations across six open-weight MLLMs and three human-annotated video dat

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#3 most recent of 628 cs.CL papers we have recorded · ↑ newer: ARLoop: Fully-Open Pre-trained Looped Transformers with Attention Resi · ↓ older: When Self-Verification Hurts: Calibrated Answer Repair for Financial Q

Cite this page: What Does the Model See? Representation Validity in Multimodal LLM-Based Video Analysis: the #3 most recent of 628 cs.CL papers we have recorded (as of 2026-11-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/what-does-the-model-see-representation-validity-in-multimodal-llm-based-video-an.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json

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