AIConfPaper
← Search

Fantine Huot

6 accepted papers

2025

Agents' Room: Narrative Generation through Multi-step Collaboration

Fantine Huot, Reinald Kim Amplayo, Jennimaria Palomaki, Alice Shoshana Jakobovits, Elizabeth Clark, Mirella Lapata
ICLR 2025poster

Writing compelling fiction is a multifaceted process combining elements such as crafting a plot, developing interesting characters, and using evocative language. While large language models (LLMs) show promise for story writing, they currently rely heavily on intricate prompting, which limits their…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2025

Help Me Write a Story: Evaluating LLMs’ Ability to Generate Writing Feedback

Hannah Rashkin, Elizabeth Clark, Fantine Huot, Mirella Lapata
ACL 2025long

Can LLMs provide support to creative writers by giving meaningful writing feedback? In this paper, we explore the challenges and limitations of model-generated writing feedback by defining a new task, dataset, and evaluation frameworks. To study model performance in a controlled manner, we present a…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts

Sumit Asthana, Hannah Rashkin, Elizabeth Clark, Fantine Huot, Mirella Lapata
EMNLP 2024main

One useful application of NLP models is to support people in reading complex text from unfamiliar domains (e.g., scientific articles). Simplifying the entire text makes it understandable but sometimes removes important details. On the contrary, helping adult readers understand difficult concepts in…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2024

Learning to Plan and Generate Text with Citations

Constanza Fierro, Reinald Kim Amplayo, Fantine Huot, Nicola De Cao, Joshua Maynez, Shashi Narayan +1
ACL 2024long

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 supporting evidence. In this paper, we explore the attribution capabilities of plan-based models which have been recent…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2024

Low-Rank Adaptation for Multilingual Summarization: An Empirical Study

Chenxi Whitehouse, Fantine Huot, Jasmijn Bastings, Mostafa Dehghani, Chu-Cheng Lin, Mirella Lapata
NAACL 2024findings

Although the advancements of pre-trained Large Language Models have significantly accelerated recent progress in NLP, their ever-increasing size poses significant challenges for conventional fine-tuning, especially in memory-intensive tasks. We investigate the potential of Parameter-Efficient Fine-T…

Cited by 4Source ↗PDF ↗Scholar ↗
2023

Scaling Vision Transformers to 22 Billion Parameters

Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer +36
ICML 2023oral

The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Vision Transformers (ViT) have introduced the same architecture to image and video modelling, but these have not yet been suc…

Cited by 650Source ↗PDF ↗Scholar ↗