708 citations · 732 across the 6 of their papers we have counts for
6 papers
Characterizing Tradeoffs in Language Model Decoding with Informational Interpretations
Chung-Ching Chang, William W. Cohen, Yun-Hsuan Sung
We propose a theoretical framework for formulating language model decoder algorithms with dynamic programming and information theory. With dynamic programming, we lift the design o…
Hallucination Augmented Recitations for Language Models
Abdullatif Köksal, Renat Aksitov, Chung-Ching Chang
Attribution is a key concept in large language models (LLMs) as it enables control over information sources and enhances the factuality of LLMs. While existing approaches utilize o…
KL-Divergence Guided Temperature Sampling
Chung-Ching Chang, David Reitter, Renat Aksitov +1
Temperature sampling is a conventional approach to diversify large language model predictions. As temperature increases, the prediction becomes diverse but also vulnerable to hallu…
Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models
Renat Aksitov, Chung-Ching Chang, David Reitter +2
Despite recent progress, it has been difficult to prevent semantic hallucinations in generative Large Language Models. One common solution to this is augmenting LLMs with a retriev…
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization
Dheeraj Rajagopal, Siamak Shakeri, Cicero Nogueira dos Santos +2
Abstractive summarization systems based on pretrained language models often generate coherent but factually inconsistent sentences. In this paper, we present a counterfactual data…
LaMDA: Language Models for Dialog Applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall +57
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters an…