Zobrazeno 1 - 10
of 6 492
pro vyhledávání: '"Consistency evaluation"'
Multi-regional clinical trial (MRCT) has been common practice for drug development and global registration. The FDA guidance "Demonstrating Substantial Evidence of Effectiveness for Human Drug and Biological Products Guidance for Industry" (FDA, 2019
Externí odkaz:
http://arxiv.org/abs/2411.15567
Autor:
Sreekar, P Aditya, Verma, Sahil, Chopra, Suransh, Ghazarian, Sarik, Persad, Abhishek, Sadagopan, Narayanan
Large Language Models (LLMs) are widely used in both industry and academia for various tasks, yet evaluating the consistency of generated text responses continues to be a challenge. Traditional metrics like ROUGE and BLEU show a weak correlation with
Externí odkaz:
http://arxiv.org/abs/2409.16984
Autor:
Agarwal, Raunak
This work addresses the challenge of factual consistency in text generation systems. We unify the tasks of Natural Language Inference, Summarization Evaluation, Factuality Verification and Factual Consistency Evaluation to train models capable of eva
Externí odkaz:
http://arxiv.org/abs/2408.04114
While deep neural networks have achieved remarkable performance, they tend to lack transparency in prediction. The pursuit of greater interpretability in neural networks often results in a degradation of their original performance. Some works strive
Externí odkaz:
http://arxiv.org/abs/2408.04600
Publikováno v:
KDD 2024 (oral)
The prevailing issue of factual inconsistency errors in conventional Retrieval Augmented Generation (RAG) motivates the study of Factual Consistency Evaluation (FCE). Despite the various FCE methods proposed earlier, these methods are evaluated on da
Externí odkaz:
http://arxiv.org/abs/2407.01080
Image inpainting, the task of reconstructing missing segments in corrupted images using available data, faces challenges in ensuring consistency and fidelity, especially under information-scarce conditions. Traditional evaluation methods, heavily dep
Externí odkaz:
http://arxiv.org/abs/2405.16263
Akademický článek
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Kniha
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Factual inconsistency with source documents in automatically generated summaries can lead to misinformation or pose risks. Existing factual consistency(FC) metrics are constrained by their performance, efficiency, and explainability. Recent advances
Externí odkaz:
http://arxiv.org/abs/2402.13758
Autor:
Cui, Wendi, Zhang, Jiaxin, Li, Zhuohang, Damien, Lopez, Das, Kamalika, Malin, Bradley, Kumar, Sricharan
Evaluating the quality and variability of text generated by Large Language Models (LLMs) poses a significant, yet unresolved research challenge. Traditional evaluation methods, such as ROUGE and BERTScore, which measure token similarity, often fail t
Externí odkaz:
http://arxiv.org/abs/2401.02132