AraSEG is a genre-diverse Arabic sentence segmentation corpus showing lightweight encoders and dependency parsers outperform LLMs under challenging punctuation while improving downstream parsing.
Preprint, arXiv:2503.04793
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The paper introduces the Proxy Compression Hypothesis as a unifying framework explaining reward hacking in RLHF as an emergent result of compressing high-dimensional human objectives into proxy reward signals under optimization pressure.
Inserting sentence-boundary delimiters into LLM inputs yields consistent gains of up to 7.7% on GSM8k and 12.5% on DROP across 7B to 600B models via in-context learning and supervised fine-tuning.
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Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges
The paper introduces the Proxy Compression Hypothesis as a unifying framework explaining reward hacking in RLHF as an emergent result of compressing high-dimensional human objectives into proxy reward signals under optimization pressure.