#autoregressive-decoding News & Analysis

3 articles tagged with #autoregressive-decoding. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AINeutralarXiv – CS AI · Jun 117/10

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From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data

Researchers identify three core architectural mechanisms in large language models that systematically produce hallucinations: self-attention's statistical confusion of entities, maximum likelihood training that rewards plausible-sounding falsehoods, and autoregressive decoding that cascades errors forward. Dataset quality issues amplify rather than originate these failures, suggesting that fixing hallucinations requires architectural redesign, not just better training data.

AIBullisharXiv – CS AI · Jun 107/10

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K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling

Researchers introduce K-Forcing, a novel language modeling approach that enables autoregressive models to generate multiple tokens simultaneously rather than sequentially, achieving 2.4-3.5x inference speedup. The technique distills existing AR models into a push-forward mapping trained via progressive self-forcing, maintaining compatibility with standard serving infrastructure while trading modest quality for significant computational efficiency gains critical for industrial-scale LLM deployment.

AIBullisharXiv – CS AI · Jun 56/10

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DiG-Plan: Mitigating Early Commitment for Tool-Graph Planning via Diffusion Guidance

Researchers introduce DiG-Plan, a novel framework addressing the early commitment problem in tool-graph planning by combining diffusion-based proposal generation with autoregressive refinement. The approach improves solution coverage from 32% to 94.3% and delivers 10% relative gains over traditional autoregressive baselines on TaskBench benchmarks.