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Found 10 papers, 3 papers with code
PHAnToM: Personality Has An Effect on Theory-of-Mind Reasoning in Large Language Models
Drawing inspiration from psychological research on the links between certain personality traits and Theory-of-Mind (ToM) reasoning, and from prompt engineering research on the hyper-sensitivity of prompts in affecting LLMs capabilities, this study investigates how inducing personalities in LLMs using prompts affects their ToM reasoning capabilities.
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey
Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding.
HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent
The data generation pipeline is transferable and can be easily adapted for labeled conversation data generation in other domains.
Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation
We explore the correlation between the level of hallucination in model responses and two types of sequence-level certainty: probabilistic certainty and semantic certainty.
DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM
Additionally, by exploiting the power of diffusion model, our framework also provides the capability to do topic based text generation.
S2vNTM: Semi-supervised vMF Neural Topic Modeling
Across a variety of datasets, S2vNTM outperforms existing semi-supervised topic modeling methods in classification accuracy with limited keywords provided.
KDSTM: Neural Semi-supervised Topic Modeling with Knowledge Distillation
In text classification tasks, fine tuning pretrained language models like BERT and GPT-3 yields competitive accuracy; however, both methods require pretraining on large text datasets.
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning
Despite progress in differential privacy and generative modeling for privacy-preserving data release in the literature, only a few approaches optimize for machine learning utility: most approaches only take into account statistical metrics on the data itself and fail to explicitly preserve the loss metrics of machine learning models that are to be subsequently trained on the generated data.
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporating human knowledge.
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FFPDG: Fast, Fair and Private Data Generation
Generative modeling has been used frequently in synthetic data generation.
