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Related papers: Efficacy of Synthetic Data as a Benchmark

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Reinforcement Learning (RL) has been shown to significantly boost reasoning capabilities of large language models (LLMs) in math, coding, and multi-hop reasoning tasks. However, RL fine-tuning requires abundant high-quality verifiable data,…

Training data plays a crucial role in Large Language Models (LLM) scaling, yet high quality data is of limited supply. Synthetic data techniques offer a potential path toward sidestepping these limitations. We conduct a large-scale…

Cognitive assessments require normative data as essential benchmarks for evaluating individual performance. Hence, developing new cognitive tests based on novel image stimuli is challenging due to the lack of readily available normative…

Machine Learning · Computer Science 2025-09-09 Victoria Yan , Honor Chotkowski , Fengran Wang , Xinhui Li , Carl Yang , Jiaying Lu , Runze Yan , Xiao Hu , Alex Fedorov

This paper presents a comprehensive evaluation of the capabilities of Large Language Models (LLMs) in metaphor interpretation across multiple datasets, tasks, and prompt configurations. Although metaphor processing has gained significant…

Computation and Language · Computer Science 2025-07-22 Elisa Sanchez-Bayona , Rodrigo Agerri

Meta-analyses statistically aggregate the findings of different randomized controlled trials (RCTs) to assess treatment effectiveness. Because this yields robust estimates of treatment effectiveness, results from meta-analyses are…

Computation and Language · Computer Science 2024-07-26 Hye Sun Yun , David Pogrebitskiy , Iain J. Marshall , Byron C. Wallace

Large Language Models (LLMs), with gradually improving reading comprehension and reasoning capabilities, are being applied to a range of complex language tasks, including the automatic generation of language data for various purposes.…

Computation and Language · Computer Science 2024-11-11 Long Truong To , Hung Tuan Le , Dat Van-Thanh Nguyen , Manh Trong Nguyen , Tri Thien Nguyen , Tin Van Huynh , Kiet Van Nguyen

Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to generate human-like text has opened new possibilities for market research, particularly in…

Artificial Intelligence · Computer Science 2026-04-20 Mengxin Wang , Dennis J. Zhang , Heng Zhang

Predicting human daily behavior is challenging due to the complexity of routine patterns and short-term fluctuations. While data-driven models have improved behavior prediction by leveraging empirical data from various platforms and…

Machine Learning · Computer Science 2025-05-26 Haoxin Li , Jingtao Ding , Jiahui Gong , Yong Li

Student modeling is central to many educational technologies as it enables predicting future learning outcomes and designing targeted instructional strategies. However, open-ended learning domains pose challenges for accurately modeling…

Computation and Language · Computer Science 2024-05-07 Manh Hung Nguyen , Sebastian Tschiatschek , Adish Singla

Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes time given the large amounts of data. LLMs are increasingly…

The number and dynamic nature of web and mobile applications presents significant challenges for assessing their compliance with data protection laws. In this context, symbolic and statistical Natural Language Processing (NLP) techniques…

Computation and Language · Computer Science 2025-12-22 David Rodriguez , Ian Yang , Jose M. Del Alamo , Norman Sadeh

Data is fundamental to large language models (LLMs). However, understanding of what makes certain data useful for different stages of an LLM workflow, including training, tuning, alignment, in-context learning, etc., and why, remains an…

Artificial Intelligence · Computer Science 2026-05-20 Shiqiang Wang , Herbert Woisetschläger , Hans Arno Jacobsen , Mingyue Ji

Recent advances in large language model (LLM) training have highlighted the need for diverse, high-quality instruction data. Recently, many works are exploring synthetic data generation using LLMs. However, they primarily focus on prompt…

Computation and Language · Computer Science 2024-12-10 Yifang Chen , David Zhu , Simon Du , Kevin Jamieson , Yang Liu

Given the increasing use of synthetic data in language model (LM) post-training, an LM's ability to generate high-quality data has become nearly as crucial as its ability to solve problems directly. While prior works have focused on…

Large, human-annotated datasets are central to the development of natural language processing models. Collecting these datasets can be the most challenging part of the development process. We address this problem by introducing a general…

Computation and Language · Computer Science 2020-04-29 Alana Marzoev , Samuel Madden , M. Frans Kaashoek , Michael Cafarella , Jacob Andreas

The advancement of large language models (LLMs) has led to a greater challenge of having a rigorous and systematic evaluation of complex tasks performed, especially in enterprise applications. Therefore, LLMs need to be able to benchmark…

Computation and Language · Computer Science 2024-10-18 Bing Zhang , Mikio Takeuchi , Ryo Kawahara , Shubhi Asthana , Md. Maruf Hossain , Guang-Jie Ren , Kate Soule , Yada Zhu

Large Language Models (LLMs) show promise as data analysis agents, but existing benchmarks overlook the iterative nature of the field, where experts' decisions evolve with deeper insights of the dataset. To address this, we introduce…

Computation and Language · Computer Science 2025-06-09 Hanyu Li , Haoyu Liu , Tingyu Zhu , Tianyu Guo , Zeyu Zheng , Xiaotie Deng , Michael I. Jordan

Sentiment analysis (SA) has been a long-standing research area in natural language processing. It can offer rich insights into human sentiments and opinions and has thus seen considerable interest from both academia and industry. With the…

Computation and Language · Computer Science 2023-05-25 Wenxuan Zhang , Yue Deng , Bing Liu , Sinno Jialin Pan , Lidong Bing

Large Language Models (LLMs) excel at generating synthetic data, but ensuring its quality and diversity remains challenging. We propose Genetic Prompt, a novel framework that combines genetic algorithms with LLMs to augment synthetic data…

Computation and Language · Computer Science 2025-09-03 Guangzeng Han , Weisi Liu , Xiaolei Huang

The LLM-as-a-judge paradigm enables flexible, user-defined evaluation, but its effectiveness is often limited by the scarcity of diverse, representative data for refining criteria. We present a tool that integrates synthetic data generation…

Human-Computer Interaction · Computer Science 2025-11-07 Hyo Jin Do , Zahra Ashktorab , Jasmina Gajcin , Erik Miehling , Martín Santillán Cooper , Qian Pan , Elizabeth M. Daly , Werner Geyer