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Found 7 papers, 4 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.
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.
Some Practice for Improving the Search Results of E-commerce
In the Amazon KDD Cup 2022, we aim to apply natural language processing methods to improve the quality of search results that can significantly enhance user experience and engagement with search engines for e-commerce.
TLab: Traffic Map Movie Forecasting Based on HR-NET
In our 2020 Competition solution, we further design multiple variants based on HR-NET and UNet.
Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge Solution
How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem.
Efficient Project Gradient Descent for Ensemble Adversarial Attack
Consider $l_2$ norms attacks, Project Gradient Descent (PGD) and the Carlini and Wagner (C\&W) attacks are the two main methods, where PGD control max perturbation for adversarial examples while C\&W approach treats perturbation as a regularization term optimized it with loss function together.
