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Found 9 papers, 1 papers with code
CaM-Gen: Causally Aware Metric-Guided Text Generation
We leverage causal inference techniques to identify causally significant aspects of a text that lead to the target metric and then explicitly guide generative models towards these by a feedback mechanism.
DynamicTOC: Persona-based Table of Contents for Consumption of Long Documents
Linearly consuming (via scrolling or navigation through default table of content) these documents is time-consuming and challenging.
Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences
Explainable NLP techniques primarily explain by answering "Which tokens in the input are responsible for this prediction?''.
Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong
To reduce over-reliance on LLMs, we ask LLMs to provide contrastive information - explain both why the claim is true and false, and then we present both sides of the explanation to users.
The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Features
In this work, we study the effect of the presence of protected and proxy features on participants' perception of model fairness and their ability to improve demographic parity over an AI alone.
What Else Do I Need to Know? The Effect of Background Information on Users' Reliance on QA Systems
In this work, we study how users interact with QA systems in the absence of sufficient information to assess their predictions.
Personalized Detection of Cognitive Biases in Actions of Users from Their Logs: Anchoring and Recency Biases
The recognition of cognitive bias in computer science is largely in the domain of information retrieval, and bias is identified at an aggregate level with the help of annotated data.
CaM-Gen:Causally-aware Metric-guided Text Generation
We leverage causal inference techniques to identify causally significant aspects of a text that lead to the target metric and then explicitly guide generative models towards these by a feedback mechanism.
Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus
Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain corpus.
