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Found 23 papers, 7 papers with code
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System
To tackle the challenge of accurate and timely communication regarding the COVID-19 pandemic, we present a COVID-19 Claim Radar to automatically extract supporting and refuting claims on a daily basis.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
Lifelong Event Detection with Knowledge Transfer
We focus on lifelong event detection as an exemplar case and propose a new problem formulation that is also generalizable to other IE tasks.
Do Language Models Have Bayesian Brains? Distinguishing Stochastic and Deterministic Decision Patterns within Large Language Models
Building on this assumption, prior research has used simulated Gibbs sampling, inspired by experiments designed to elicit human priors, to infer the priors of language models.
Ordered-subsets Multi-diffusion Model for Sparse-view CT Reconstruction
Experimental results demonstrate that OSMM outperforms traditional diffusion models in terms of image quality and noise resilience, offering a powerful and versatile solution for advanced CT imaging in sparse-view scenarios.
LDGen: Enhancing Text-to-Image Synthesis via Large Language Model-Driven Language Representation
In this paper, we introduce LDGen, a novel method for integrating large language models (LLMs) into existing text-to-image diffusion models while minimizing computational demands.
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.
Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production
In the rapidly evolving field of metabolic engineering, the quest for efficient and precise gene target identification for metabolite production enhancement presents significant challenges.
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).
Why Does New Knowledge Create Messy Ripple Effects in LLMs?
Extensive previous research has focused on post-training knowledge editing (KE) for language models (LMs) to ensure that knowledge remains accurate and up-to-date.
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.
MuChin: A Chinese Colloquial Description Benchmark for Evaluating Language Models in the Field of Music
To this end, we present MuChin, the first open-source music description benchmark in Chinese colloquial language, designed to evaluate the performance of multimodal LLMs in understanding and describing music.
PET Tracer Conversion among Brain PET via Variable Augmented Invertible Network
Meanwhile, the invertible network iteratively estimates the resultant DOPA PET data and compares it to the reference DOPA PET data.
Defining a New NLP Playground
The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history.
Information Association for Language Model Updating by Mitigating LM-Logical Discrepancy
To evaluate and address the core challenge, we propose a new task formulation of the information updating task that only requires the provision of an unstructured updating corpus and evaluates the performance of information updating on the generalizability to question-answer pairs pertaining to the updating information.
RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought
To detect factual inconsistency, RCoT first asks LLMs to reconstruct the problem based on generated solutions.
SongDriver: Real-time Music Accompaniment Generation without Logical Latency nor Exposure Bias
In this paper, we propose SongDriver, a real-time music accompaniment generation system without logical latency nor exposure bias.
MUSE: Textual Attributes Guided Portrait Painting Generation
Experiments show that our approach can accurately illustrate 78% textual attributes, which also help MUSE capture the subject in a more creative and expressive way.
AuxBlocks: Defense Adversarial Example via Auxiliary Blocks
Deep learning models are vulnerable to adversarial examples, which poses an indisputable threat to their applications.
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers.
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention
In this paper, we aim to incorporate the hierarchical information of relations for distantly supervised relation extraction and propose a novel hierarchical attention scheme.
Tracking of enriched dialog states for flexible conversational information access
The above observations motivate us to enrich current representation of dialog states and collect a brand new dialog dataset about movies, based upon which we build a new DST, called enriched DST (EDST), for flexible accessing movie information.
