Intercepting the Kangaroo: Experimental Astrolinguistics with Constructed Lexicons, Active Probing, and Large Language Models as Informants and Hypothesis Proposers
Read the original on arXiv AI →The study turns the speculative field of astrolinguistics into an experiment by using two large language models with deliberately incompatible constructed lexicons as informants. A scripted orchestrator translates between the two category systems, and a protocol combining cross‑situational elimination, predictive probes, active scene selection, and a stricter recovery round successfully prevents the ‘kangaroo effect’—the silent attachment of a word to the wrong referent—in over 400 simulated and live runs. When informant noise is introduced, the protocol remains robust up to 2% per‑word noise and largely abstains rather than errs at higher noise levels, while a generate‑and‑test loop allows recovery of words outside the scripted hypothesis space, achieving full coverage as the rule‑proposing LLM’s capability increases. whyItMatters":"The protocol demonstrates that experimental astrolinguistics can reliably avoid mistranslations and recover unknown terms, showing that correctness is governed by the protocol while coverage depends on the instruments used."
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