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Order-agnostic language models show order-dependent likelihoods

By PulseAugur Editorial · [1 sources] ·

Researchers have identified that order-agnostic language models (OALMs) do not perfectly factorize joint distributions, meaning the order in which tokens are revealed can impact the generated likelihood by up to 0.49 nats/token. While confidence-first decoding is order-agnostic, its token reveal order closely resembles left-to-right generation. The study also proposes a new diagnostic tool based on the variance of confidence traces, showing that uniform confidence spreading maximizes target recoverability and that lower variance correlates with higher downstream correctness. AI

IMPACT Reveals fundamental limitations in order-agnostic language models, potentially guiding future research in decoding strategies and model evaluation.

RANK_REASON Academic paper detailing novel findings about language model behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Order-agnostic language models show order-dependent likelihoods

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Academic paper detailing novel findings about language model behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Lin Yao ·

    Decoding in Order-Agnostic Language Models: Chain-Rule Deviation and Uniform Spreading

    arXiv:2606.00997v1 Announce Type: new Abstract: Order-agnostic language models (OALMs), including discrete diffusion language models (dLLMs), are trained to predict masked tokens under arbitrary conditioning sets, allowing sequences to be generated or scored under arbitrary revea…

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