Bidirectional LLM-GNN co-teaching with round-based pseudo-label preference optimization outperforms golden-teacher baselines on few-shot TAG benchmarks by 3-8% absolute gains.
arXiv preprint arXiv:2412.14922 , year=
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CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
REALM jointly learns model parameters and per-annotator expertise scalars during fine-tuning by modeling observed labels as mixtures of model predictions and uniform noise, improving accuracy under simulated annotation noise.
Label noise hurts fine-tuning performance most while grammatical and typographical noise sometimes act as mild regularizers, with changes concentrated in task-specific layers.
citing papers explorer
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Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching
Bidirectional LLM-GNN co-teaching with round-based pseudo-label preference optimization outperforms golden-teacher baselines on few-shot TAG benchmarks by 3-8% absolute gains.
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Common-agency Games for Multi-Objective Test-Time Alignment
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
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REALM: Reliable Expertise-Aware Language Model Fine-Tuning from Noisy Annotations
REALM jointly learns model parameters and per-annotator expertise scalars during fine-tuning by modeling observed labels as mixtures of model predictions and uniform noise, improving accuracy under simulated annotation noise.
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Analyzing the Effect of Noise in LLM Fine-tuning
Label noise hurts fine-tuning performance most while grammatical and typographical noise sometimes act as mild regularizers, with changes concentrated in task-specific layers.