HEAL mitigates entropy collapse in few-shot RLVR by selectively adding general-domain data and aligning trajectory-level entropy dynamics, matching full-shot performance with 32 target samples.
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LLMs exhibit systematic failures in obeying expressed certainty in retrieved contexts, but a combination of prior reminders, certainty recalibration, and context simplification reduces obedience errors by 25%.
VLMs recover reliable population-level trends in climate change visual discourse on social media even when per-image accuracy is only moderate.
citing papers explorer
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HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment
HEAL mitigates entropy collapse in few-shot RLVR by selectively adding general-domain data and aligning trajectory-level entropy dynamics, matching full-shot performance with 32 target samples.
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Can LLMs Take Retrieved Information with a Grain of Salt?
LLMs exhibit systematic failures in obeying expressed certainty in retrieved contexts, but a combination of prior reminders, certainty recalibration, and context simplification reduces obedience errors by 25%.
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From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media
VLMs recover reliable population-level trends in climate change visual discourse on social media even when per-image accuracy is only moderate.