A distributional alignment metric d_NTP and a linear regression method LTV for task vectors that improves accuracy by 9.2% over baselines on classification and regression tasks across multiple LLMs.
A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A convex KMM-based valuation method that accounts for both target-task alignment and inter-dataset redundancy in gradient space outperforms standard gradient-alignment baselines for LLM post-training data selection.
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Distributional Alignment as a Criterion for Designing Task Vectors in In-Context Learning
A distributional alignment metric d_NTP and a linear regression method LTV for task vectors that improves accuracy by 9.2% over baselines on classification and regression tasks across multiple LLMs.
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Convex Dataset Valuation for Post-Training
A convex KMM-based valuation method that accounts for both target-task alignment and inter-dataset redundancy in gradient space outperforms standard gradient-alignment baselines for LLM post-training data selection.