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Found 10 papers, 4 papers with code
Say ‘YES’ to Positivity: Detecting Toxic Language in Workplace Communications
Specifically, the lack of corpus, sparsity of toxicity in enterprise emails, and well-defined criteria for annotating toxic conversations have prevented researchers from addressing the problem at scale.
ROBBIE: Robust Bias Evaluation of Large Generative Language Models
In this work, our focus is two-fold: (1) Benchmarking: a comparison of 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative LLMs.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models
Large-scale Pre-Trained Language Models (PTLMs) capture knowledge from massive human-written data which contains latent societal biases and toxic contents.
Compositional Generalization for Natural Language Interfaces to Web APIs
We define new compositional generalization tasks for NL2API which explore the models' ability to extrapolate from simple API calls in the training set to new and more complex API calls in the inference phase.
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding
We demonstrate that while recent models reach human performance when they have access to large amounts of labeled data, there is a huge gap in performance in the few-shot setting for most tasks.
Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback
We study the task of semantic parse correction with natural language feedback.
Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer
In this paper, we study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications.
On Domain Transfer When Predicting Intent in Text
In many domains, especially enterprise text analysis, there is an abundance of data which can be used for the development of new AI-powered intelligent experiences to improve people's productivity.
Online Distributed Optimization on Dynamic Networks
This paper presents a distributed optimization scheme over a network of agents in the presence of cost uncertainties and over switching communication topologies.
