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Found 25 papers, 10 papers with code
Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions
From this, we present a conceptual framework of "Bidirectional Human-AI Alignment" to organize the literature from a human-centered perspective.
UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback
Our method starts with an existing LLM and iteratively produces improved models by self-generating a large synthetic dataset using an original model, applying automated tools to aggressively filter, score, and de-duplicate the data into a refined higher quality dataset.
Deconstructing the Veneer of Simplicity: Co-Designing Introductory Generative AI Workshops with Local Entrepreneurs
Generative AI platforms and features are permeating many aspects of work.
GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence
We present GenAudit -- a tool intended to assist fact-checking LLM responses for document-grounded tasks.
"What's important here?": Opportunities and Challenges of Using LLMs in Retrieving Information from Web Interfaces
Large language models (LLMs) that have been trained on a corpus that includes large amount of code exhibit a remarkable ability to understand HTML code.
Latent Phrase Matching for Dysarthric Speech
Many consumer speech recognition systems are not tuned for people with speech disabilities, resulting in poor recognition and user experience, especially for severe speech differences.
USB: A Unified Summarization Benchmark Across Tasks and Domains
While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to control and reliability.
Downstream Datasets Make Surprisingly Good Pretraining Corpora
In experiments addressing both ELECTRA and RoBERTa models and 10 distinct downstream classification datasets, we observe that self-pretraining rivals standard pretraining on the BookWiki corpus (despite using around $10\times$--$500\times$ less data), outperforming the latter on $7$ and $5$ datasets, respectively.
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets.
Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation
Target-guided response generation enables dialogue systems to smoothly transition a conversation from a dialogue context toward a target sentence.
Nonverbal Sound Detection for Disordered Speech
Voice assistants have become an essential tool for people with various disabilities because they enable complex phone- or tablet-based interactions without the need for fine-grained motor control, such as with touchscreens.
Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation
Experiments on classification, ranking and evaluation tasks across multiple datasets demonstrate that our approaches outperform strong baselines in providing informative negative examples for training dialogue systems.
SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter
The ability to automatically detect stuttering events in speech could help speech pathologists track an individual's fluency over time or help improve speech recognition systems for people with atypical speech patterns.
Predicting risk of dyslexia with an online gamified test
Dyslexia is a specific learning disorder related to school failure.
Controlling Dialogue Generation with Semantic Exemplars
Dialogue systems pretrained with large language models generate locally coherent responses, but lack the fine-grained control over responses necessary to achieve specific goals.
Extracting Structured Data from Physician-Patient Conversations By Predicting Noteworthy Utterances
In this exploratory study, we describe a new dataset consisting of conversation transcripts, post-visit summaries, corresponding supporting evidence (in the transcript), and structured labels.
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques
Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes.
InstructableCrowd: Creating IF-THEN Rules for Smartphones via Conversations with the Crowd
The user verbally expresses a problem to the system, in which a group of crowd workers collectively respond and program relevant multi-part IF-THEN rules to help the user.
StateLens: A Reverse Engineering Solution for Making Existing Dynamic Touchscreens Accessible
Second, using the state diagrams, StateLens automatically generates conversational agents to guide blind users through specifying the tasks that the interface can perform, allowing the StateLens iOS application to provide interactive guidance and feedback so that blind users can access the interface.
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References
The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation.
VizWiz Grand Challenge: Answering Visual Questions from Blind People
The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings.
Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time
Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs.
"Is there anything else I can help you with?": Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent
To address this problem, we developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system.
Real-time On-Demand Crowd-powered Entity Extraction
Output-agreement mechanisms such as ESP Game have been widely used in human computation to obtain reliable human-generated labels.
