MLLMs ignore dial state geometry and cluster by appearance, causing inconsistency under variations; TriSCA's state-distance alignment, metadata supervision, and objective alignment improve robustness on clock and gauge benchmarks.
Gonzalez, Ion Stoica, and Eric P
4 Pith papers cite this work. Polarity classification is still indexing.
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bLLMs achieve state-of-the-art results on limited and imbalanced SE sentiment datasets even in zero-shot settings, but fine-tuned sLLMs outperform when ample balanced training data is available.
DFLOP is a data-driven framework that profiles data-induced computation variance and uses predictive scheduling to balance workloads in multimodal LLM training pipelines, claiming up to 3.6x faster training than existing frameworks.
A survey that categorizes RAG methods for LLMs into four retrieval-centric stages, reviews their evolution and evaluation, and outlines challenges and future directions.
citing papers explorer
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State Beyond Appearance: Diagnosing and Improving State Consistency in Dial-Based Measurement Reading
MLLMs ignore dial state geometry and cluster by appearance, causing inconsistency under variations; TriSCA's state-distance alignment, metadata supervision, and objective alignment improve robustness on clock and gauge benchmarks.
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Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models
bLLMs achieve state-of-the-art results on limited and imbalanced SE sentiment datasets even in zero-shot settings, but fine-tuned sLLMs outperform when ample balanced training data is available.
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DFLOP: A Data-driven Framework for Multimodal LLM Training Pipeline Optimization
DFLOP is a data-driven framework that profiles data-induced computation variance and uses predictive scheduling to balance workloads in multimodal LLM training pipelines, claiming up to 3.6x faster training than existing frameworks.
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A Survey on Retrieval-Augmented Text Generation for Large Language Models
A survey that categorizes RAG methods for LLMs into four retrieval-centric stages, reviews their evolution and evaluation, and outlines challenges and future directions.