arXiv AI

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 AI

Jun 24

Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability

arXiv:2606. 23701v1 Announce Type: cross Abstract: Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure.

By Sherri Weitl-Harms, John Hastings

llms nlp safety

arXiv AI

Aug 10

Measuring Concept Content in Text from LLM Activations: ESG Evidence from Concept Vectors and Linear Probes

arXiv:2608. 07208v1 Announce Type: cross Abstract: Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similarities.

By Luc Hazenoot, Zhaochun Ren, Amirhossein Zohrehvand

llms rag fine-tuning

arXiv Machine Learning

Jun 5

Can LLMs Write Correct TLA+ Specifications? Evaluating Natural-Language-to-TLA+ Generation

arXiv:2606. 05792v1 Announce Type: cross Abstract: TLA+ has supported industrial verification at companies such as Amazon and Microsoft, yet writing correct TLA+ specifications from natural language still requires time and expertise, which limits adoption.

By Arslan Bisharat, Brian Ortiz, Eric Spencer, Khushboo Bhadauria, TaiNing Wang, George K. Thiruvathukal, Konstantin Laufer, Mohammed Abuhamad

llms safety

arXiv AI

Jul 8

Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities

arXiv:2607. 06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings.

By So Hasegawa, Shailaja Keyur Sampat, Lei Liu, Wei-Peng Chen

llms agents benchmarks

arXiv AI

Jul 24

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

arXiv:2512. 10092v2 Announce Type: replace Abstract: Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases in training data.

By Nick Jiang, Xiaoqing Sun, Lisa Dunlap, Lewis Smith, Neel Nanda

llms rag

arXiv AI

1d ago

Faster, Cheaper, More Accurate: Specialised Knowledge Tracing Models Outperform LLMs

arXiv:2603. 02830v2 Announce Type: replace-cross Abstract: Predicting future student responses to questions is particularly valuable for educational learning platforms where it enables effective interventions.

By Prarthana Bhattacharyya, Joshua Mitton, Ralph Abboud, Simon Woodhead

llms efficiency

arXiv AI

Jun 30

LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals

arXiv:2411. 10109v3 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes.

By Joon Sung Park, Carolyn Q. Zou, Jonne Kamphorst, Niles Egan, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Percy Liang, Robb Willer, Michael S. Bernstein

llms agents benchmarks

arXiv AI

Aug 3

Human-LLM Collaborative Inductive Coding for Conceptualizing K-12 Educator AI Use

arXiv:2607. 28889v1 Announce Type: cross Abstract: Qualitative researchers increasingly encounter interaction corpora whose scale exceeds what manual coding alone can address, and large language models (LLMs) are frequently proposed as analytic assistants.

By Alex Liu, Min Sun, Lief Esbenshade, Michael Xiao, Victor Tian, Zachary Zhang, Kevin He

llms agents

arXiv AI

Jun 8

DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment in Debate Scenarios

arXiv:2606. 07226v1 Announce Type: cross Abstract: Human creativity has emerged as a critical competency in the era of large language models.

By Tongzhou Yu, Mingjia Li, Hong Qian, Wenkai Wang, Zongbao Zhang, Yaoyu Jiang, Xiangfeng Wang, Aimin Zhou, Jiajun Guo

llms benchmarks safety

arXiv AI

Jul 8

Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs

arXiv:2607. 06327v1 Announce Type: cross Abstract: Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing research has predominantly focused on English.

By Andrea Alfarano, Andrea Bacciu, Saab Mansour, Amin Mantrach, Marcello Federico

llms rag

arXiv AI

Jun 24

Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules

arXiv:2508. 10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs.

By Nasim Shirvani-Mahdavi, Chengkai Li

llms fine-tuning

arXiv AI

Jul 17

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.

By Inder Preet, Shuxin Lin, Dhaval Patel

llms nlp benchmarks

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