565 citations · 813 across the 15 of their papers we have counts for
18 papers
Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing Feedback
Hannah Rashkin, Elizabeth Clark, Fantine Huot +1
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…
Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts
Sumit Asthana, Hannah Rashkin, Elizabeth Clark +2
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 understa…
DoubleDipper: Improving Long-Context LLMs via Context Recycling
Arie Cattan, Alon Jacovi, Alex Fabrikant +8
Despite recent advancements in Large Language Models (LLMs), their performance on tasks involving long contexts remains sub-optimal. In this work, we propose DoubleDipper, a novel…
Investigating Content Planning for Navigating Trade-offs in Knowledge-Grounded Dialogue
Kushal Chawla, Hannah Rashkin, Gaurav Singh Tomar +1
Knowledge-grounded dialogue generation is a challenging task because it requires satisfying two fundamental yet often competing constraints: being responsive in a manner that is sp…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Measuring Attribution in Natural Language Generation Models
Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm +7
With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG outpu…
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Zeqiu Wu, Yi Luan, Hannah Rashkin +4
Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each questio…
Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features
Hannah Rashkin, David Reitter, Gaurav Singh Tomar +1
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…
Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark
Nouha Dziri, Hannah Rashkin, Tal Linzen +1
Knowledge-grounded dialogue systems powered by large language models often generate responses that, while fluent, are not attributable to a relevant source of information. Progress…
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction
Xinyao Ma, Maarten Sap, Hannah Rashkin +1
Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction. For example, a female character in a stor…
PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking
Hannah Rashkin, Asli Celikyilmaz, Yejin Choi +1
We propose the task of outline-conditioned story generation: given an outline as a set of phrases that describe key characters and events to appear in a story, the task is to gener…
Abductive Commonsense Reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya +6
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…
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap +3
We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et…
Defending Against Neural Fake News
Rowan Zellers, Ari Holtzman, Hannah Rashkin +4
Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also migh…
Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
Hannah Rashkin, Eric Michael Smith, Margaret Li +1
One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill. While it is straightforward for humans to…
Modeling Naive Psychology of Characters in Simple Commonsense Stories
Hannah Rashkin, Antoine Bosselut, Maarten Sap +2
Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people's mental states - a capability that i…
Event2Mind: Commonsense Inference on Events, Intents, and Reactions
Hannah Rashkin, Maarten Sap, Emily Allaway +2
We investigate a new commonsense inference task: given an event described in a short free-form text ("X drinks coffee in the morning"), a system reasons about the likely intents ("…
Connotation Frames: A Data-Driven Investigation
Hannah Rashkin, Sameer Singh, Yejin Choi
Through a particular choice of a predicate (e.g., "x violated y"), a writer can subtly connote a range of implied sentiments and presupposed facts about the entities x and y: (1) w…