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Found 17 papers, 13 papers with code
CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense
To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.
Ranked #1 on
Common Sense Reasoning
on CODAH
(using extra training data)
CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense
To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.
Multi-sense Definition Modeling using Word Sense Decompositions
Word embeddings capture syntactic and semantic information about words.
Model selection for deep audio source separation via clustering analysis
We compare our confidence-based ensemble approach to using individual models with no selection, to an oracle that always selects the best model and to a random model selector.
Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation
In this paper, we present augmentative generation (Aug-Gen), a method of dataset augmentation for any music generation system trained on a resource-constrained domain.
Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach
Deep generative systems that learn probabilistic models from a corpus of existing music do not explicitly encode knowledge of a musical style, compared to traditional rule-based systems.
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
Despite recent advances in natural language generation, it remains challenging to control attributes of generated text.
Generated Knowledge Prompting for Commonsense Reasoning
It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models.
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
Starting with an existing dataset, MultiNLI for natural language inference (NLI), our approach uses dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instructs GPT-3 to compose new examples with similar patterns.
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Applying our method to the vanilla GPT3, we demonstrate a 33% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT-001, which was trained with private user data and human annotations.
Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts
Text detoxification has the potential to mitigate the harms of toxicity by rephrasing text to remove offensive meaning, but subtle toxicity remains challenging to tackle.
We're Afraid Language Models Aren't Modeling Ambiguity
We find that the task remains extremely challenging, including for GPT-4, whose generated disambiguations are considered correct only 32% of the time in human evaluation, compared to 90% for disambiguations in our dataset.
How Language Model Hallucinations Can Snowball
A major risk of using language models in practical applications is their tendency to hallucinate incorrect statements.
Inverse Scaling: When Bigger Isn't Better
Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e. g., due to flaws in the training objective and data.
That was the last straw, we need more: Are Translation Systems Sensitive to Disambiguating Context?
In experiments, we compare MT-specific models and language models for (i) their preference when given an ambiguous subsentence, (ii) their sensitivity to disambiguating context, and (iii) the performance disparity between figurative and literal source sentences.
Tuning Language Models by Proxy
Despite the general capabilities of large pretrained language models, they consistently benefit from further adaptation to better achieve desired behaviors.
A Taxonomy of Ambiguity Types for NLP
Ambiguity is an critical component of language that allows for more effective communication between speakers, but is often ignored in NLP.
