Search Results for author:
Found 18 papers, 8 papers with code
Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs
Recent advances in large language models (LLM) have enabled richer social simulations, allowing for the study of various social phenomena with LLM-based agents.
Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
The interactive use of large language models (LLMs) in AI assistants (at work, home, etc.)
FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions
Theory of mind (ToM) evaluations currently focus on testing models using passive narratives that inherently lack interactivity.
SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence.
WebArena: A Realistic Web Environment for Building Autonomous Agents
Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions.
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements
To study the contextual dynamics of offensiveness, we train models to generate COBRA explanations, with and without access to the context.
Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models
The escalating debate on AI's capabilities warrants developing reliable metrics to assess machine "intelligence".
Don't Take This Out of Context! On the Need for Contextual Models and Evaluations for Stylistic Rewriting
Most existing stylistic text rewriting methods and evaluation metrics operate on a sentence level, but ignoring the broader context of the text can lead to preferring generic, ambiguous, and incoherent rewrites.
Learning to translate by learning to communicate
We formulate and test a technique to use Emergent Communication (EC) with a pre-trained multilingual model to improve on modern Unsupervised NMT systems, especially for low-resource languages.
Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection
The perceived toxicity of language can vary based on someone's identity and beliefs, but this variation is often ignored when collecting toxic language datasets, resulting in dataset and model biases.
Extracting and Inferring Personal Attributes from Dialogue
Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes.
Challenges in Automated Debiasing for Toxic Language Detection
Overall, our findings show that debiasing a model trained on biased toxic language data is not as effective as simply relabeling the data to remove existing biases.
Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets
Although large-scale pretrained language models, such as BERT and RoBERTa, have achieved superhuman performance on in-distribution test sets, their performance suffers on out-of-distribution test sets (e. g., on contrast sets).
Multilevel Text Alignment with Cross-Document Attention
Text alignment finds application in tasks such as citation recommendation and plagiarism detection.
RPD: A Distance Function Between Word Embeddings
Based on the properties of RPD, we study the relations of word embeddings of different algorithms systematically and investigate the influence of different training processes and corpora.
Evaluating Commonsense in Pre-trained Language Models
However, relatively little work has been done investigating commonsense knowledge contained in contextualized representations, which is crucial for human question answering and reading comprehension.
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
The system is built on the pa-rameter server architecture and aims to speed up the model training via utilizing a cluster of servers in case of large volume of training data.
Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation
In conclusion, the proposed CNN-GCN method combines local image information with graph connectivity information, improving pulmonary A/V separation over a baseline CNN method, approaching the performance of human observers.
Ranked #1 on
Pulmonary Artery–Vein Classification
on SunYs
3D Medical Imaging Segmentation
Pulmonary Artery–Vein Classification
