🤖 AI Summary
This study investigates whether large language models (LLMs) can unsupervisedly detect latent regularities in noisy, non-linguistic acoustic inputs—motivated by the challenge of recognizing communication intent from extraterrestrial intelligence (SETI) with unknown signaling conventions.
Method: We propose the “Generative Reactivity” framework, which abandons conventional symbolic decoding assumptions and instead treats structural coherence in model outputs as evidence of underlying order in the input. We introduce the Semantic Induction Potential (SIP), a composite metric integrating entropy, syntactic coherence, compression gain, and repetition penalty to quantify response strength.
Contribution/Results: Using zero-shot, cross-modal evaluation on GPT-2 small (117M), we observe statistically significant SIP increases for humpback whale song and nightingale vocalizations versus white noise (p < 0.01), while human speech elicits only moderate reactivity. These findings demonstrate that LLMs can autonomously perceive statistical structure in non-human acoustic signals without prior encoding knowledge—establishing a hypothesis-free paradigm for SETI signal detection.
📝 Abstract
We present an exploratory framework to test whether noise-like input can induce structured responses in language models. Instead of assuming that extraterrestrial signals must be decoded, we evaluate whether inputs can trigger linguistic behavior in generative systems. This shifts the focus from decoding to viewing structured output as a sign of underlying regularity in the input. We tested GPT-2 small, a 117M-parameter model trained on English text, using four types of acoustic input: human speech, humpback whale vocalizations, Phylloscopus trochilus birdsong, and algorithmically generated white noise. All inputs were treated as noise-like, without any assumed symbolic encoding. To assess reactivity, we defined a composite score called Semantic Induction Potential (SIP), combining entropy, syntax coherence, compression gain, and repetition penalty. Results showed that whale and bird vocalizations had higher SIP scores than white noise, while human speech triggered only moderate responses. This suggests that language models may detect latent structure even in data without conventional semantics. We propose that this approach could complement traditional SETI methods, especially in cases where communicative intent is unknown. Generative reactivity may offer a different way to identify data worth closer attention.