arXiv Machine Learning
Using Small Language Models to Reverse-Engineer Machine Learning Pipelines Structures
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arXiv AI
Jul 15
So Many Opinions, So Many LLMs: Comparing Large Language Models to Traditional Machine Learning for Open- Ended Survey Analysis
arXiv:2607. 11890v1 Announce Type: cross Abstract: Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale.
arXiv Machine Learning
Sep 30
It's All Training: A Fully Synthetic Single-Stage Recipe for LLMs
arXiv:2609.37891v1 Announce Type: cross Abstract: Current pre-training datasets are derived from web crawls, with all their issues, and were not designed to support mid- and post-training pipelines--...
arXiv AI
Jul 10
Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry
arXiv:2601. 22588v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used as reference-free evaluators via prompting, but this "LLM-as-a-Judge" paradigm is costly, opaque, and sensitive to prompt design.
arXiv AI
Aug 21
Improved Confidence Estimates for Black-Box Large Language Models
arXiv:2608. 19323v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs).
arXiv Computation and Language
Oct 8
How Do LLMs Change Predictions Under Negation?
arXiv:2610.09571v1 Announce Type: new Abstract: Negation is an essential feature of human language, yet large language models (LLMs) remain unreliable in processing it. We evaluate recent open-source...
arXiv AI
Sep 24
Evaluation of pre-trained models for pedagogical assessment of novel AI-assisted educational questions
The study evaluates how well pre‑trained models can classify the Bloom level of AI‑generated educational questions, a task that is crucial for ensuring pedagogical quality. Traditional machine‑learning models perform poorly on out‑of‑distribution data, whereas transformer and large‑language models achieve higher accuracy, especially after feature‑engineering techniques such as text splicing and appending learning objectives. Retraining the models yields the most significant performance gains across all datasets.