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Found 7 papers, 2 papers with code
Characterizing Tradeoffs in Language Model Decoding with Informational Interpretations
We propose a theoretical framework for formulating language model decoder algorithms with dynamic programming and information theory.
Memory Augmented Language Models through Mixture of Word Experts
We demonstrate that MoWE performs significantly better than the T5 family of models with similar number of FLOPs in a variety of NLP tasks.
Hallucination Augmented Recitations for Language Models
For open book QA as a case study, we demonstrate that models finetuned with our counterfactual datasets improve text grounding, leading to better open book QA performance, with up to an 8. 0% increase in F1 score.
KL-Divergence Guided Temperature Sampling
One common approach to mitigate hallucinations is to provide source/grounding documents and the model is trained to produce predictions that bind to and are attributable to the provided source.
Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models
One common solution to this is augmenting LLMs with a retrieval system and making sure that the generated output is attributable to the retrieved information.
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization
Abstractive summarization systems based on pretrained language models often generate coherent but factually inconsistent sentences.
LaMDA: Language Models for Dialog Applications
We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.
Ranked #113 on
Code Generation
on HumanEval
