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Found 13 papers, 5 papers with code
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention
It is hypothesized that one’s interests in a hashtag are related with what they said before (user history) and the existing posts present the hashtag (hashtag contexts).
Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics
This paper studies social emotions to online discussion topics.
The Law of Knowledge Overshadowing: Towards Understanding, Predicting, and Preventing LLM Hallucination
To address it, we propose a novel concept: knowledge overshadowing, where model's dominant knowledge can obscure less prominent knowledge during text generation, causing the model to fabricate inaccurate details.
Internal Activation as the Polar Star for Steering Unsafe LLM Behavior
Large language models (LLMs) have demonstrated exceptional capabilities across a wide range of tasks but also pose significant risks due to their potential to generate harmful content.
EscapeBench: Pushing Language Models to Think Outside the Box
Language model agents excel in long-session planning and reasoning, but existing benchmarks primarily focus on goal-oriented tasks with explicit objectives, neglecting creative adaptation in unfamiliar environments.
Integrative Decoding: Improve Factuality via Implicit Self-consistency
Self-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective in improving the factual accuracy of large language models.
A Survey on the Honesty of Large Language Models
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to faithfully express their knowledge.
Knowledge Overshadowing Causes Amalgamated Hallucination in Large Language Models
This phenomenon partially stems from training data imbalance, which we verify on both pretrained models and fine-tuned models, over a wide range of LM model families and sizes. From a theoretical point of view, knowledge overshadowing can be interpreted as over-generalization of the dominant conditions (patterns).
EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries
The dynamic nature of real-world information necessitates efficient knowledge editing (KE) in large language models (LLMs) for knowledge updating.
VIBE: Topic-Driven Temporal Adaptation for Twitter Classification
Language features are evolving in real-world social media, resulting in the deteriorating performance of text classification in dynamics.
Time Will Change Things: An Empirical Study on Dynamic Language Understanding in Social Media Classification
Language features are ever-evolving in the real-world social media environment.
Knowledge Graph-based Neurodegenerative Diseases and Diet Relationship Discovery
To date, there are no effective treatments for most neurodegenerative diseases.
Engage the Public: Poll Question Generation for Social Media Posts
This paper presents a novel task to generate poll questions for social media posts.
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