27 citations · 161 across the 39 of their papers we have counts for
12 papers · 1 filter
Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment
Philippe Laban, Lidiya Murakhovs'ka, Caiming Xiong +1
The interactive nature of Large Language Models (LLMs) theoretically allows models to refine and improve their answers, yet systematic analysis of the multi-turn behavior of LLMs r…
Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems
Lidiya Murakhovs'ka, Philippe Laban, Tian Xie +2
Making big purchases requires consumers to research or consult a salesperson to gain domain expertise. However, existing conversational recommender systems (CRS) often overlook use…
Automatic and Human-AI Interactive Text Generation
Yao Dou, Philippe Laban, Claire Gardent +1
In this tutorial, we focus on text-to-text generation, a class of natural language generation (NLG) tasks, that takes a piece of text as input and then generates a revision that is…
Beyond the Chat: Executable and Verifiable Text-Editing with LLMs
Philippe Laban, Jesse Vig, Marti A. Hearst +2
Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing. However, standard chat-based conver…
XGen-7B Technical Report
Erik Nijkamp, Tian Xie, Hiroaki Hayashi +22
Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LL…
Art or Artifice? Large Language Models and the False Promise of Creativity
Tuhin Chakrabarty, Philippe Laban, Divyansh Agarwal +2
Researchers have argued that large language models (LLMs) exhibit high-quality writing capabilities from blogs to stories. However, evaluating objectively the creativity of a piece…
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles
Kung-Hsiang Huang, Philippe Laban, Alexander R. Fabbri +4
Previous research in multi-document news summarization has typically concentrated on collating information that all sources agree upon. However, the summarization of diverse inform…
Next Steps for Human-Centered Generative AI: A Technical Perspective
Xiang 'Anthony' Chen, Jeff Burke, Ruofei Du +8
Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI). We contribute a comprehensive research agenda that lays…
Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning
Fan Yin, Jesse Vig, Philippe Laban +3
Large language models (LLMs) have shown impressive performance in following natural language instructions to solve unseen tasks. However, it remains unclear whether models truly un…
SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages
Philippe Laban, Jesse Vig, Wojciech Kryscinski +3
Text simplification research has mostly focused on sentence-level simplification, even though many desirable edits - such as adding relevant background information or reordering co…
LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond
Philippe Laban, Wojciech Kryściński, Divyansh Agarwal +4
With the recent appearance of LLMs in practical settings, having methods that can effectively detect factual inconsistencies is crucial to reduce the propagation of misinformation…
Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questions
Philippe Laban, Chien-Sheng Wu, Lidiya Murakhovs'ka +2
Modern news aggregators do the hard work of organizing a large news stream, creating collections for a given news story with tens of source options. This paper shows that navigatin…