LLM-Qualia-Benchmark: Analyzing the Limits of LLMs in Qualia and Epistemology
Introduction & Context (The Problem & Motivation) Presentation There are problems that are easy for a machine to solve and extremely complex for a human being; likewise, there are trivial tasks for a human that prove virtually impossible for a machine to truly comprehend. Good examples of this asymmetry can be observed in daily life: instantly calculating the exact route for thousands of…
Title: LLM-Qualia-Benchmark: Investigating the Limits of LLMs in Qualia and Epistemology
The research article "LLM-Qualia-Benchmark: Analyzing the Limits of LLMs in Qualia and Epistemology" explores the disparity between human and machine capabilities. The author highlights that while machines excel at specific tasks, such as calculating optimal delivery routes, they struggle with understanding nuances like sarcasm or reacting to unpredictable obstacles.
This observation ties into the ongoing debate between Weak AI and Strong AI, with the former referring to systems that simulate cognitive processes without consciousness, and the latter representing a theoretical machine possessing genuine mind, phenomenological consciousness (qualia), and the ability to comprehend the intrinsic meaning of processed information.
The primary objective of this study is to investigate the boundaries of Language Models (LLMs) in terms of qualia and epistemology. The author identifies the challenge as organizing qualitative data from LLMs and making rigorous human-centric inferences. The purpose is to evaluate the limitations of algorithmic systems, demonstrating that without human discernment, grounding, and validation, machines operate solely at the level of syntactic symbol manipulation, lacking real semantic comprehension or phenomenological experience.
The researchers conducted a benchmark using two LLMs: openai/gpt-oss-120b and qwen/qwen3.8-27b. The benchmark consisted of 15 conceptual questions organized into three thematic axes (5 questions per block) to test the capability boundaries of these models. The first block, P1 — Perception, Sensation, and Qualia, measured the models' ability to differentiate between descriptive knowledge and pure sensory experiences, or qualia.
The second block, P2 — Limits and Paradoxes of Human Simulation, assessed the models' capacity to navigate philosophical dilemmas in the philosophy of mind, such as Searle's Chinese Room, simulated pain, and ethics detached from mortality.
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