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Found 13 papers, 2 papers with code
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
SpeCrawler: Generating OpenAPI Specifications from API Documentation Using Large Language Models
By creating a standardized format for numerous APIs, SpeCrawler aids in streamlining integration processes within API orchestrating systems and facilitating the incorporation of tools into LLMs.
What's the Plan? Evaluating and Developing Planning-Aware Techniques for LLMs
Planning is a fundamental task in artificial intelligence that involves finding a sequence of actions that achieve a specified goal in a given environment.
Unveiling Safety Vulnerabilities of Large Language Models
As large language models become more prevalent, their possible harmful or inappropriate responses are a cause for concern.
Predicting Question-Answering Performance of Large Language Models through Semantic Consistency
Semantic consistency of a language model is broadly defined as the model's ability to produce semantically-equivalent outputs, given semantically-equivalent inputs.
Reliable and Interpretable Drift Detection in Streams of Short Texts
Data drift is the change in model input data that is one of the key factors leading to machine learning models performance degradation over time.
Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora
The ability to compare the semantic similarity between text corpora is important in a variety of natural language processing applications.
Understanding the Properties of Generated Corpora
Models for text generation have become focal for many research tasks and especially for the generation of sentence corpora.
Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems
The rapidly growing market demand for automatic dialogue agents capable of goal-oriented behavior has caused many tech-industry leaders to invest considerable efforts into task-oriented dialog systems.
Classifier Data Quality: A Geometric Complexity Based Method for Automated Baseline And Insights Generation
Testing Machine Learning (ML) models and AI-Infused Applications (AIIAs), or systems that contain ML models, is highly challenging.
Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead
We show that intent prediction can be improved by training a deep text-to-text neural model to generate successive user utterances from unlabeled dialogue data.
We've had this conversation before: A Novel Approach to Measuring Dialog Similarity
The results demonstrate that our method outperforms the other approaches in capturing dialog flow, and is better aligned with the human perception of conversation similarity.
Not Enough Data? Deep Learning to the Rescue!
Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks.
