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Found 11 papers, 5 papers with code
Why and How LLMs Hallucinate: Connecting the Dots with Subsequence Associations
Large language models (LLMs) frequently generate hallucinations-content that deviates from factual accuracy or provided context-posing challenges for diagnosis due to the complex interplay of underlying causes.
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
In this paper, we present the first unified platform, MMDT (Multimodal DecodingTrust), designed to provide a comprehensive safety and trustworthiness evaluation for MMFMs.
KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual Checking
This paper introduces KnowHalu, a novel approach for detecting hallucinations in text generated by large language models (LLMs), utilizing step-wise reasoning, multi-formulation query, multi-form knowledge for factual checking, and fusion-based detection mechanism.
Blockchain Large Language Models
This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions.
Grounded Graph Decoding Improves Compositional Generalization in Question Answering
Grounding enables the model to retain syntax information from the input in thereby significantly improving generalization over complex inputs.
Kokoyi: Executable LaTeX for End-to-end Deep Learning
Despite substantial efforts from the deep learning system community to relieve researchers and practitioners from the burden of implementing models with ever-growing complexity, a considerable lingual gap remains between developing models in the language of mathematics and implementing them in the languages of computer.
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training
Recent work has shown that the training of a one-hidden-layer, scalar-output fully-connected ReLU neural network can be reformulated as a finite-dimensional convex program.
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
We show that the FQT gradient is an unbiased estimator of the QAT gradient, and we discuss the impact of gradient quantization on its variance.
Ranked #9 on
Semantic Textual Similarity
on STS Benchmark
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Ranked #34 on
Node Classification
on Cora
Gradient-based learning for F-measure and other performance metrics
Many important classification performance metrics, e. g. $F$-measure, are non-differentiable and non-decomposable, and are thus unfriendly to gradient descent algorithm.
Loss Functions for Multiset Prediction
In this paper, we propose a novel multiset loss function by viewing this problem from the perspective of sequential decision making.
