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Hannah Rashkin

6 accepted papers

2025

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

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…

2024

Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts

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…

2022

CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning

EMNLP 2022main

Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context. Moreover, it can be expensive to re-train well-established retrie…

2021

Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features

ACL 2021long

Knowledge-grounded dialogue systems are intended to convey information that is based on evidence provided in a given source text. We discuss the challenges of training a generative neural dialogue model for such systems that is controlled to stay faithful to the evidence. Existing datasets contain a…

Cited by 115SourcePDFScholar
2020

Abductive Commonsense Reasoning

ICLR 2020poster

Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanat…

Cited by 468SourceScholar
2019

Defending Against Neural Fake News

NeurIPS 2019poster

Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable adversaries to generate neural fake news: targeted propaganda that closely mimics the style of real news.