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 novel test set of 1,300 stories that we corrupted to intentionally introduce writing issues.We study the performance of commonly used LLMs in this task with both automatic and human evaluation metrics.Our analysis shows that current models have strong out-of-the-box behavior in many respects-providing specific and mostly accurate writing feedback.However, models often fail to identify the biggest writing issue in the story and to correctly decide when to offer critical vs. positive feedback.

Kushal Chawla, Hannah Rashkin, Gaurav Singh Tomar, David Reitter. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

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 context can enhance their vocabulary and knowledge.In a preliminary human study, we first identify that lack of context and unfamiliarity with difficult concepts is a major reason for adult readers' difficulty with domain-specific text.We then introduce targeted concept simplification, a simplification task for rewriting text to help readers comprehend text containing unfamiliar concepts.We also introduce WIKIDOMAINS 1 , a new dataset of 22k definitions from 13 academic domains paired with a difficult concept within each definition.We benchmark the performance of open-source and commercial LLMs, and a simple dictionary baseline on this task across human judgments of ease of understanding and meaning preservation.Interestingly, our human judges preferred explanations about the difficult concept more than simplification of the concept phrase.Further, no single model achieved superior performance across all quality dimensions, and automated metrics also show low correlations with human evaluations of concept simplification (∼ 0.2), opening up rich avenues for research on personalized human reading comprehension support.* Work done as student researcher at Google DeepMind. 1 https://github.com/google-deepmind/wikidomains

Abstract Large neural models have brought a new challenge to natural language generation (NLG): It has become imperative to ensure the safety and reliability of the output of models that generate freely. To this end, we present an evaluation framework, Attributable to Identified Sources (AIS), stipulating that NLG output pertaining to the external world is to be verified against an independent, provided source. We define AIS and a two-stage annotation pipeline for allowing annotators to evaluate model output according to annotation guidelines. We successfully validate this approach on generation datasets spanning three tasks (two conversational QA datasets, a summarization dataset, and a table-to-text dataset). We provide full annotation guidelines in the appendices and publicly release the annotated data at https://github.com/google-research-datasets/AIS.

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 retrain well-established retrievers such as search engines that are originally developed for nonconversational queries.To facilitate their use, we develop a query rewriting model CONQRR that rewrites a conversational question in the context into a standalone question.It is trained with a novel reward function to directly optimize towards retrieval using reinforcement learning and can be adapted to any off-theshelf retriever.CONQRR achieves state-ofthe-art results on a recent open-domain CQA dataset containing conversations from three different sources, and is effective for two different off-the-shelf retrievers.Our extensive analysis also shows the robustness of CON-QRR to out-of-domain dialogues as well as to zero query rewriting supervision.

Abstract 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 towards models that do not exhibit this issue requires evaluation metrics that can quantify its prevalence. To this end, we introduce the Benchmark for Evaluation of Grounded INteraction (Begin), comprising 12k dialogue turns generated by neural dialogue systems trained on three knowledge-grounded dialogue corpora. We collect human annotations assessing the extent to which the models’ responses can be attributed to the given background information. We then use Begin to analyze eight evaluation metrics. We find that these metrics rely on spurious correlations, do not reliably distinguish attributable abstractive responses from unattributable ones, and perform substantially worse when the knowledge source is longer. Our findings underscore the need for more sophisticated and robust evaluation metrics for knowledge-grounded dialogue. We make Begin publicly available at https://github.com/google/BEGIN-dataset.

Hannah Rashkin, David Reitter, Gaurav Singh Tomar, Dipanjan Das. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

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 story is often portrayed as passive and powerless ("She daydreams about being a doctor") while a man is portrayed as more proactive and powerful ("He pursues his dream of being a doctor").We formulate Controllable Debiasing, a new revision task that aims to rewrite a given text to correct the implicit and potentially undesirable bias in character portrayals.We then introduce POWERTRANSFORMER as an approach that debiases text through the lens of connotation frames (Sap et al., 2017), which encode pragmatic knowledge of implied power dynamics with respect to verb predicates.One key challenge of our task is the lack of parallel corpora.To address this challenge, we adopt an unsupervised approach using auxiliary supervision with related tasks such as paraphrasing and self-supervision based on a reconstruction loss, building on pretrained language models.Through comprehensive experiments based on automatic and human evaluations, we demonstrate that our approach outperforms ablations and existing methods from related tasks.Furthermore, we demonstrate the use of POWER-TRANSFORMER as a step toward mitigating the well-documented gender bias in character portrayal in movie scripts.

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 generate a coherent narrative that is consistent with the provided outline.This task is challenging as the input only provides a rough sketch of the plot, and thus, models need to generate a story by interweaving the key points provided in the outline.This requires the model to keep track of the dynamic states of the latent plot, conditioning on the input outline while generating the full story.We present PLOTMACHINES, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states.In addition, we enrich PLOTMACHINES with high-level discourse structure so that the model can learn different writing styles corresponding to different parts of the narrative.Comprehensive experiments over three fiction and non-fiction datasets demonstrate that large-scale language models, such as GPT-2 and GROVER, despite their impressive generation performance, are not sufficient in generating coherent narratives for the given outline, and dynamic plot state tracking is important for composing narratives with tighter, more consistent plots.

We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., “if X pays Y a compliment, then Y will likely return the compliment”). We propose nine if-then relation types to distinguish causes vs. effects, agents vs. themes, voluntary vs. involuntary events, and actions vs. mental states. By generatively training on the rich inferential knowledge described in ATOMIC, we show that neural models can acquire simple commonsense capabilities and reason about previously unseen events. Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.

We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and Con-ceptNet (Speer et al., 2017).Contrary to many conventional KBs that store knowledge with canonical templates, commonsense KBs only store loosely structured open-text descriptions of knowledge.We posit that an important step toward automatic commonsense completion is the development of generative models of commonsense knowledge, and propose COMmonsEnse Transformers (COMET ) that learn to generate rich and diverse commonsense descriptions in natural language.Despite the challenges of commonsense modeling, our investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs.Empirical results demonstrate that COMET is able to generate novel knowledge that humans rate as high quality, with up to 77.5% (ATOMIC) and 91.7% (ConceptNet) precision at top 1, which approaches human performance for these resources.Our findings suggest that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods.

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 recognize and acknowledge others' feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publicly-available datasets for training and evaluation.This work proposes a new benchmark for empathetic dialogue generation and EMPATHETICDIALOGUES, a novel dataset of 25k conversations grounded in emotional situations.Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, compared to models merely trained on large-scale Internet conversation data.We also present empirical comparisons of dialogue model adaptations for empathetic responding, leveraging existing models or datasets without requiring lengthy retraining of the full model.

Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, Yejin Choi. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

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. Modern computer security relies on careful threat modeling: identifying potential threats and vulnerabilities from an adversary's point of view, and exploring potential mitigations to these threats. Likewise, developing robust defenses against neural fake news requires us first to carefully investigate and characterize the risks of these models. We thus present a model for controllable text generation called Grover. Given a headline like 'Link Found Between Vaccines and Autism,' Grover can generate the rest of the article; humans find these generations to be more trustworthy than human-written disinformation. Developing robust verification techniques against generators like Grover is critical. We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data. Counterintuitively, the best defense against Grover turns out to be Grover itself, with 92% accuracy, demonstrating the importance of public release of strong generators. We investigate these results further, showing that exposure bias -- and sampling strategies that alleviate its effects -- both leave artifacts that similar discriminators can pick up on. We conclude by discussing ethical issues regarding the technology, and plan to release Grover publicly, helping pave the way for better detection of neural fake news.

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 is trivial for humans but remarkably hard for machines.To facilitate research addressing this challenge, we introduce a new annotation framework to explain naive psychology of story characters as fully-specified chains of mental states with respect to motivations and emotional reactions.Our work presents a new largescale dataset with rich low-level annotations and establishes baseline performance on several new tasks, suggesting avenues for future research.

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 ("X wants to stay awake") and reactions ("X feels alert") of the event's participants.To support this study, we construct a new crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations.We report baseline performance on this task, demonstrating that neural encoder-decoder models can successfully compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants.In addition, we demonstrate how commonsense inference on people's intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts. 1 https://tinyurl.com/event2mind

We present an analytic study on the language of news media in the context of political fact-checking and fake news detection.We compare the language of real news with that of satire, hoaxes, and propaganda to find linguistic characteristics of untrustworthy text.To probe the feasibility of automatic political fact-checking, we also present a case study based on PolitiFact.com using their factuality judgments on a 6-point scale.Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.

The framing of an action influences how we perceive its actor.We introduce connotation frames of power and agency, a pragmatic formalism organized using frame semantic representations, to model how different levels of power and agency are implicitly projected on actors through their actions.We use the new power and agency frames to measure the subtle, but prevalent, gender bias in the portrayal of modern film characters and provide insights that deviate from the well-known Bechdel test.Our contributions include an extended lexicon of connotation frames along with a web interface that provides a comprehensive analysis through the lens of connotation frames.

We present a new approach for documentlevel sentiment inference, where the goal is to predict directed opinions (who feels positively or negatively towards whom) for all entities mentioned in a text.To encourage more complete and consistent predictions, we introduce an ILP that jointly models (1) sentence-and discourse-level sentiment cues, (2) factual evidence about entity factions, and (3) global constraints based on social science theories such as homophily, social balance, and reciprocity.Together, these cues allow for rich inference across groups of entities, including for example that CEOs and the companies they lead are likely to have similar sentiment towards others.We evaluate performance on new, densely labeled data that provides supervision for all pairs, complementing previous work that only labeled pairs mentioned in the same sentence.Experiments demonstrate that the global model outperforms sentence-level baselines, by providing more coherent predictions across sets of related entities.

Through a particular choice of a predicate (e.g., "x violated y"), a writer can subtly connote a range of implied sentiment and presupposed facts about the entities x and y: (1) writer's perspective: projecting x as an "antagonist" and y as a "victim", (2) entities' perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event.We introduce connotation frames as a representation formalism to organize these rich dimensions of connotation using typed relations.First, we investigate the feasibility of obtaining connotative labels through crowdsourcing experiments.We then present models for predicting the connotation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations.Empirical results confirm that connotation frames can be induced from various data sources that reflect how language is used in context.We conclude with analytical results that show the potential use of connotation frames for analyzing subtle biases in online news media.