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Found 16 papers, 12 papers with code
Calibrating Large Language Models with Sample Consistency
Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application.
What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception
A rationale outlines the approach followed by the model to answer the question.
ExpertQA: Expert-Curated Questions and Attributed Answers
In this work, we conduct human evaluation of responses from a few representative systems along various axes of attribution and factuality, by bringing domain experts in the loop.
QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations
To study the ability of retrieval systems to meet such information needs, we construct QUEST, a dataset of 3357 natural language queries with implicit set operations, that map to a set of entities corresponding to Wikipedia documents.
AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference
To this end, we construct AmbiCoref, a diagnostic corpus of minimal sentence pairs with ambiguous and unambiguous referents.
Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models
Cognitive psychologists have documented that humans use cognitive heuristics, or mental shortcuts, to make quick decisions while expending less effort.
Generative Data Augmentation for Commonsense Reasoning
Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance.
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The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages.
Commonsense Knowledge Base Completion with Structural and Semantic Context
Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1. 5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency.
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017).
A Simple Joint Model for Improved Contextual Neural Lemmatization
English verbs have multiple forms.
Sparse and Constrained Attention for Neural Machine Translation
In NMT, words are sometimes dropped from the source or generated repeatedly in the translation.
Neural Factor Graph Models for Cross-lingual Morphological Tagging
Morphological analysis involves predicting the syntactic traits of a word (e. g. {POS: Noun, Case: Acc, Gender: Fem}).
Learning Language Representations for Typology Prediction
One central mystery of neural NLP is what neural models "know" about their subject matter.
DyNet: The Dynamic Neural Network Toolkit
In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.
