Daniel Weld

• University of Washington

Retrieval-augmented generation is increasingly effective in answering scientific questions from literature, but many state-of-the-art systems are expensive and closed-source. We introduce Ai2 Scholar QA, a free online scientific question answering application. To facilitate research, we make our entire pipeline public: as a customizable open-source...

Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they largely explore variants of existing codebases or similarly constrained design spaces, and (2) they produce large volumes of research artifacts (such as automatically gen...

Surveying prior literature to establish a foundation for new knowledge is essential for scholarly progress. However, survey articles are resource-intensive and challenging to create, and can quickly become outdated as new research is published, risking information staleness and inaccuracy. Keeping survey articles current with the latest evidence is...

We present Cocoa, a system that implements a novel interaction design pattern -- interactive plans -- for users to collaborate with an AI agent on complex, multi-step tasks in a document editor. Cocoa harmonizes human and AI efforts and enables flexible delegation of agency through two actions: Co-planning (where users collaboratively compose a pla...

Remarkable advancements in modern generative foundation models have enabled the development of sophisticated and highly capable autonomous agents that can observe their environment, invoke tools, and communicate with other agents to solve problems. Although such agents can communicate with users through natural language, their complexity and wide-r...

When conducting literature reviews, scientists often create literature review tables - tables whose rows are publications and whose columns constitute a schema, a set of aspects used to compare and contrast the papers. Can we automatically generate these tables using language models (LMs)? In this work, we introduce a framework that leverages LMs t...

The scientific ideation process often involves blending salient aspects of existing papers to create new ideas. To see if large language models (LLMs) can assist this process, we contribute Scideator, a novel mixed-initiative tool for scientific ideation. Starting from a user-provided set of papers, Scideator extracts key facets (purposes, mechanis...

Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature grows, the greater the need for new technology to support scholars. In contrast to the process of finding papers, which has been transformed by Internet technology, the e...

The current literature on AI‐advised decision making—involving explainable AI systems advising human decision makers—presents a series of inconclusive and confounding results. To synthesize these findings, we propose a simple theory that elucidates the frequent failure of AI explanations to engender appropriate reliance and complementary decision m...

Research-paper blog posts help scientists disseminate their work to a larger audience, but translating papers into this format requires substantial additional effort. Blog post creation is not simply transforming a long-form article into a short output, as studied in most prior work on human-AI summarization. In contrast, blog posts are typically f...

Scholars need to keep up with an exponentially increasing flood of scientific papers. To aid this challenge, we introduce Scim, a novel intelligent interface that helps scholars skim papers to rapidly review and gain a cursory understanding of its contents. Scim supports the skimming process by highlighting salient content within a paper, directing...

Enabling researchers to leverage systems to overcome the limits of human cognitive capacity.

Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow an AI to take advice from humans in response to explanations are similarly useful. While both capabilities are well-developed for transparent learning models (e.g., linear models and GA ² Ms), and recent techniques (e.g., LIME and...

Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature grows, the need for new technology to support the reading process grows. In contrast to the process of finding papers, which has been transformed by Internet technology,...

Scientists and science journalists, among others, often need to make sense of a large number of papers and how they compare with each other in scope, focus, findings, or any other important factors. However, with a large corpus of papers, it's cognitively demanding to pairwise compare and contrast them all with each other. Fully automating this rev...

When reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize and make sense of the hundreds of citations encountered during literature reviews. This paper introduces CiteSee, a paper reading tool that leverages a user's publishing...

The in-context learning capabilities of LLMs like GPT-3 allow annotators to customize an LLM to their specific tasks with a small number of examples. However, users tend to include only the most obvious patterns when crafting examples, resulting in underspecified in-context functions that fall short on unseen cases. Further, it is hard to know when...

Most scientific papers are distributed in PDF format, which is by default inaccessible to blind and low vision audiences and people who use assistive reading technology. These access barriers hinder and may even deter members of these groups from pursuing careers or opportunities that necessitate the reading of technical documents. In cases where n...

The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature. We combine public and proprietary data sources using s...

The vast scale and open-ended nature of knowledge graphs (KGs) make exploratory search over them cognitively demanding for users. We introduce a new technique, polymorphic lenses, that improves exploratory search over a KG by obtaining new leverage from the existing preference models that KG-based systems maintain for recommending content. The appr...

Keeping track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of important knowledge. In biomedicine, this directly impacts human lives. To address this problem, we present a novel task of extraction and search of scientific challenges a...

Systems that can automatically define unfamiliar terms hold the promise of improving the accessibility of scientific texts, especially for readers who may lack prerequisite background knowledge. However, current systems assume a single "best" description per concept, which fails to account for the many potentially useful ways a concept can be descr...

Creating labeled natural language training data is expensive and requires significant human effort. We mine input output examples from large corpora using a supervised mining function trained using a small seed set of only 100 examples. The mining consists of two stages -- (1) a biencoder-based recall-oriented dense search which pairs inputs with p...

Researchers are expected to keep up with an immense literature, yet often find it prohibitively time-consuming to do so. This paper explores how intelligent agents can help scaffold in-situ information seeking across scientific papers. Specifically, we present Scim, an AI-augmented reading interface designed to help researchers skim papers by autom...

We stand at the foot of a significant inflection in the trajectory of scientific discovery. As society continues on its fast-paced digital transformation, so does humankind's collective scientific knowledge and discourse. We now read and write papers in digitized form, and a great deal of the formal and informal processes of science are captured di...

An important goal in the field of human-AI interaction is to help users more appropriately trust AI systems' decisions. A situation in which the user may particularly benefit from more appropriate trust is when the AI receives anomalous input or provides anomalous output. To the best of our knowledge, this is the first work towards understanding ho...

The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in long, monotonous lists of suggested papers. To improve the discovery experience we introduce multiple new methods for \em augmenting recommendations with...

Accurately extracting structured content from PDFs is a critical first step for NLP over scientific papers. Recent work has improved extraction accuracy by incorporating elementary layout information, for example, each token’s 2D position on the page, into language model pretraining. We introduce new methods that explicitly model VIsual LAyout (VIL...

The past decade has witnessed a growth in the use of knowledge graph technologies for advanced data search, data integration, and query-answering applications. The leading example of a public, general-purpose open knowledge network (aka knowledge graph) is Wikidata, which has demonstrated remarkable advances in quality and coverage over this time....

The past decade has witnessed a growth in the use of knowledge graph technologies for advanced data search, data integration, and query‐answering applications. The leading example of a public, general‐purpose open knowledge network (aka knowledge graph) is Wikidata, which has demonstrated remarkable advances in quality and coverage over this time....

ive summarization systems today produce fluent and relevant output, but often "hallucinate" statements not supported by the source text. We analyze the connection between hallucinations and training data, and find evidence that models hallucinate because they train on target summaries that are unsupported by the source. Based on our findings, we pr...

Human ratings have become a crucial resource for training and evaluating machine learning systems. However, traditional elicitation methods for absolute and comparative rating suffer from issues with consistency and often do not distinguish between uncertainty due to disagreement between annotators and ambiguity inherent to the item being rated. In...

Explanations are well-known to improve recommender systems' transparency. These explanations may be local, explaining an individual recommendation, or global, explaining the recommender model in general. Despite their widespread use, there has been little investigation into the relative benefits of these two approaches. Do they provide the same ben...

The ability to keep track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of important knowledge. In biomedicine, this directly impacts human lives. To address this problem, we present a novel task of extraction and search of scientific...

Scientific silos can hinder innovation. These information "filter bubbles" and the growing challenge of information overload limit awareness across the literature, making it difficult to keep track of even narrow areas of interest, let alone discover new ones. Algorithmic curation and recommendation, which often prioritize relevance, can further re...

Human ratings have become a crucial resource for training and evaluating machine learning systems. However, traditional elicitation methods for absolute and comparative rating suffer from issues with consistency and often do not distinguish between uncertainty due to disagreement between annotators and ambiguity inherent to the item being rated. In...

Classifying the core textual components of a scientific paper-title, author, body text, etc.-is a critical first step in automated scientific document understanding. Previous work has shown how using elementary layout information, i.e., each token's 2D position on the page, leads to more accurate classification. We introduce new methods for incorpo...

AI practitioners typically strive to develop the most accurate systems, making an implicit assumption that the AI system will function autonomously. However, in practice, AI systems often are used to provide advice to people in domains ranging from criminal justice and finance to healthcare. In such AI-advised decision making, humans and machines f...

The majority of scientific papers are distributed in PDF, which pose challenges for accessibility, especially for blind and low vision (BLV) readers. We characterize the scope of this problem by assessing the accessibility of 11,397 PDFs published 2010--2019 sampled across various fields of study, finding that only 2.4% of these PDFs satisfy all of...

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms -- a fundamental concept across the sciences encompassing activities, functions and causal relations, rang...

Determining coreference of concept mentions across multiple documents is fundamental for natural language understanding. Work on cross-document coreference resolution (CDCR) typically considers mentions of events in the news, which do not often involve abstract technical concepts that are prevalent in science and technology. These complex concepts...

Leaderboards have eased model development for many NLP datasets by standardizing their evaluation and delegating it to an independent external repository. Their adoption, however, is so far limited to tasks that can be reliably evaluated in an automatic manner. This work introduces GENIE, an extensible human evaluation leaderboard, which brings the...

Counterfactual examples have been shown to be useful for many applications, including calibrating, evaluating, and explaining model decision boundaries. However, previous methods for generating such counterfactual examples have been tightly tailored to a specific application, used a limited range of linguistic patterns, or are hard to scale. We pro...

The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers. Despite prior work on definition detection, current approaches are far from being accurate enough to use in real-world applications. In this paper, we first perform in-depth error analysis of...

Despite the central importance of research papers to scientific progress, they can be difficult to read. Comprehension is often stymied when the information needed to understand a passage resides somewhere else: in another section, or in another paper. In this work, we envision how interfaces can bring definitions of technical terms and symbols to...

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token represent...

Increasingly, organizations are pairing humans with AI systems to improve decision-making and reducing costs. Proponents of human-centered AI argue that team performance can even further improve when the AI model explains its recommendations. However, a careful analysis of existing literature reveals that prior studies observed improvements due to...

Identification of new concepts in scientific literature can help power faceted search, scientific trend analysis, knowledge-base construction, and more, but current methods are lacking. Manual identification cannot keep up with the torrent of new publications, while the precision of existing automatic techniques is too low for many applications. We...

The COVID-19 pandemic has sparked unprecedented mobilization of scientists, already generating thousands of new papers that join a litany of previous biomedical work in related areas. This deluge of information makes it hard for researchers to keep track of their own research area, let alone explore new directions. Standard search engines are desig...

We introduce TLDR generation for scientific papers, a new automatic summarization task with high source compression requiring expert background knowledge and complex language understanding. To facilitate research on this task, we introduce SciTLDR, a dataset of 3.9K TLDRs. Furthermore, we introduce a novel annotation protocol for scalably curating...

The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been...

The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been...

Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-...

Decisions made by human-AI teams (e.g., AI-advised humans) are increasingly common in high-stakes domains such as healthcare, criminal justice, and finance. Achieving high team performance depends on more than just the accuracy of the AI system: Since the human and the AI may have different expertise, the highest team performance is often reached w...

As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task have used hierarchical models to contextualize sentence representations, and Conditional Rando...

We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO). However, the...

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token represent...

Tools for Interactive Machine Learning (IML) enable end users to update models in a “rapid, focused, and incremental”—yet local—manner. In this work, we study the question of local decision making in an IML context around feature selection for a sentiment classification task. Specifically, we characterize the utility of interactive feature selectio...

Traditional approaches for ensuring high quality crowdwork have failed to achieve high-accuracy on difficult problems. Aggregating redundant answers often fails on the hardest problems when the majority is confused. Argumentation has been shown to be effective in mitigating these drawbacks. However, existing argumentation systems only support limit...

Reasoning about implied relationships (e.g. paraphrastic, common sense, encyclopedic) between pairs of words is crucial for many cross-sentence inference problems. This paper proposes new methods for learning and using embeddings of word pairs that implicitly represent background knowledge about such relationships. Our pairwise embeddings are compu...

While crowdsourcing enables data collection at scale, ensuring high-quality data remains a challenge. In particular, effective task design underlies nearly every reported crowdsourcing success, yet remains difficult to accomplish. Task design is hard because it involves a costly iterative process: identifying the kind of work output one wants, conv...

Supervised event extraction systems are limited in their accuracy due to the lack of available training data. We present a method for self-training event extraction systems by bootstrapping additional training data. This is done by taking advantage of the occurrence of multiple mentions of the same event instances across newswire articles from mult...

Stack Overflow (SO) has been a great source of natural language questions and their code solutions (i.e., question-code pairs), which are critical for many tasks including code retrieval and annotation. In most existing research, question-code pairs were collected heuristically and tend to have low quality. In this paper, we investigate a new probl...

Stack Overflow (SO) has been a great source of natural language questions and their code solutions (i.e., question-code pairs), which are critical for many tasks including code retrieval and annotation. In most existing research, question-code pairs were collected heuristically and tend to have low quality. In this paper, we investigate a new probl...

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We s...

We present POAPS, a novel planning system for defining Partially Observable Markov Decision Processes (POMDPs) that abstracts away from POMDP details for the benefit of non-expert practitioners. POAPS includes an expressive adaptive programming language based on Lisp that has constructs for choice points that can be dynamically optimized. Non-exper...

Successful online communities (e.g., Wikipedia, Yelp, and StackOverflow) can produce valuable content. However, many communities fail in their initial stages. Starting an online community is challenging because there is not enough content to attract a critical mass of active members. This paper examines methods for addressing this cold-start proble...

Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and present...

Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training data, only works with relations tha...

Information Extraction (IE) aims to automatically generate a large knowledge base from natural language text, but progress remains slow. Supervised learning requires copious human annotation, while unsupervised and weakly supervised approaches do not deliver competitive accuracy. As a result, most fielded applications of IE, as well as the leading...