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Found 12 papers, 1 papers with code
Is that really a question? Going beyond factoid questions in NLP
Research in NLP has mainly focused on factoid questions, with the goal of finding quick and reliable ways of matching a query to an answer.
generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation
Large language models (LLMs) are widely deployed in various downstream tasks, e. g., auto-completion, aided writing, or chat-based text generation.
SyntaxShap: Syntax-aware Explainability Method for Text Generation
We adopt a model-based evaluation to compare SyntaxShap and its weighted form to state-of-the-art explainability methods adapted to text generation tasks, using diverse metrics including faithfulness, complexity, coherency, and semantic alignment of the explanations to the model.
Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges
We quantitatively show the value of exposing the beam search tree and present five detailed analysis scenarios addressing the identified challenges.
Negation, Coordination, and Quantifiers in Contextualized Language Models
With the success of contextualized language models, much research explores what these models really learn and in which cases they still fail.
Visual Comparison of Language Model Adaptation
The simplicity of adapter training and composition comes along with new challenges, such as maintaining an overview of adapter properties and effectively comparing their produced embedding spaces.
Beware the Rationalization Trap! When Language Model Explainability Diverges from our Mental Models of Language
Language models learn and represent language differently than humans; they learn the form and not the meaning.
Explaining Contextualization in Language Models using Visual Analytics
Despite the success of contextualized language models on various NLP tasks, it is still unclear what these models really learn.
XplaiNLI: Explainable Natural Language Inference through Visual Analytics
Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is.
ParHistVis: Visualization of Parallel Multilingual Historical Data
The study of language change through parallel corpora can be advantageous for the analysis of complex interactions between time, text domain and language.
VIANA: Visual Interactive Annotation of Argumentation
The results show that experts prefer our system over existing solutions due to the speedup provided by the automatic suggestions and the tight integration between text and graph views.
lingvis.io - A Linguistic Visual Analytics Framework
We present a modular framework for the rapid-prototyping of linguistic, web-based, visual analytics applications.
