52 citations · 158 across the 19 of their papers we have counts for
21 papers
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
Learning to Plan and Generate Text with Citations
Constanza Fierro, Reinald Kim Amplayo, Fantine Huot +4
The increasing demand for the deployment of LLMs in information-seeking scenarios has spurred efforts in creating verifiable systems, which generate responses to queries along with…
Calibrating Likelihoods towards Consistency in Summarization Models
Polina Zablotskaia, Misha Khalman, Rishabh Joshi +4
Despite the recent advances in abstractive text summarization, current summarization models still suffer from generating factually inconsistent summaries, reducing their utility fo…
PLAN: Summarizing using a Content Plan as Cross-Lingual Bridge
Fantine Huot, Joshua Maynez, Chris Alberti +5
Cross-lingual summarization consists of generating a summary in one language given an input document in a different language, allowing for the dissemination of relevant content acr…
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation
Fantine Huot, Joshua Maynez, Shashi Narayan +6
While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelev…
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study
Polina Zablotskaia, Du Phan, Joshua Maynez +3
Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty. This means that they assign hi…