A new research paper explores how alignment in large language models (LLMs) leads to reduced output diversity. The study introduces the Branching Factor (BF) metric, which quantifies the number of plausible next steps during generation. Findings indicate that BF often decreases as generation progresses, a phenomenon largely attributed to autoregressive self-conditioning rather than alignment itself. However, alignment tuning significantly sharpens the output distribution from the start, reducing BF and making models less sensitive to decoding strategies. This effect is observed to stabilize Chain-of-Thought reasoning in models like DeepSeek-distilled versions. AI
IMPACT Provides a new metric (Branching Factor) to understand and control LLM output diversity, clarifying the effects of alignment and self-conditioning.
RANK_REASON Research paper published on arXiv detailing a new metric for LLM output diversity. [lever_c_demoted from research: ic=1 ai=1.0]
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