Breaking the Likelihood Trap: Variance-Calibrated Modulation for Large Language Model Decoding
arXiv:2606.22511v2 Announce Type: replace Abstract: In open-ended generation, LLMs frequently fall into the "likelihood trap", marked by repetitive degeneration and vocabulary dullness, creating a discrepancy between machine-generated and human-written text. While post-hoc tail truncation (e.g., Top-$p$, Min-$p$) avoids sampling from the unreliable tail, it can over-sample from the uncalibrated head and misalign generation with human lexical preferences; fixed scalar repetition penalties likewise ignore variation in logit scale across inference steps, potentially disrupting semantic coherence.
Ongoing research into large language model generation quality seeks methods to improve coherence and human-like output, addressing known failure modes like the 'likelihood trap'.
Improving LLM decoding directly enhances the utility and reliability of generative AI systems, impacting a wide range of applications from content creation to autonomous agents.
This research proposes a new method to produce more diverse and semantically coherent LLM outputs, potentially reducing the 'dullness' and 'repetitiveness' currently observed in many models.
- · AI developers
- · Generative AI platforms
- · Content creators using AI
- · AI research institutions
- · Models reliant on naive decoding strategies
- · Users frustrated by repetitive AI content
Large language models will generate more varied and human-like text outputs, reducing obvious 'AI-isms'.
Improved generation quality could accelerate the adoption and integration of LLMs into more sensitive or creative human workflows.
As AI-generated content becomes indistinguishable from human work, it could amplify societal debates around authenticity, synthetic media, and digital provenance.
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Read at arXiv cs.CL