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Found 10 papers, 5 papers with code
The Generative AI Paradox: "What It Can Create, It May Not Understand"
Specifically, we propose and test the Generative AI Paradox hypothesis: generative models, having been trained directly to reproduce expert-like outputs, acquire generative capabilities that are not contingent upon -- and can therefore exceed -- their ability to understand those same types of outputs.
A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents
Many real-world applications (e. g., note taking, search) require extracting a sentence or paragraph from a document and showing that snippet to a human outside of the source document.
Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communication
Our findings have implications for designing task-appropriate communication assistance systems.
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts
They take LLM embeddings as input and output continuous prompts that are used to query the LLM.
Ensembles and Cocktails: Robust Finetuning for Natural Language Generation
When finetuning a pretrained language model for natural language generation tasks, one is currently faced with a tradeoff.
Refining Targeted Syntactic Evaluation of Language Models
Targeted syntactic evaluation of subject-verb number agreement in English (TSE) evaluates language models' syntactic knowledge using hand-crafted minimal pairs of sentences that differ only in the main verb's conjugation.
Optimal Assistance for Object-Rearrangement Tasks in Augmented Reality
We introduce a novel framework for computing and displaying AR assistance that consists of (1) associating an optimal action sequence with the policy of an embodied agent and (2) presenting this sequence to the user as suggestions in the AR system's heads-up display.
The EOS Decision and Length Extrapolation
Extrapolation to unseen sequence lengths is a challenge for neural generative models of language.
Communication-based Evaluation for Natural Language Generation
Natural language generation (NLG) systems are commonly evaluated using n-gram overlap measures (e. g. BLEU, ROUGE).
