Papers by David Wadden
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction (2023.acl-long)
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Ashish Sharma, Kevin Rushton, Inna Lin, David Wadden, Khendra Lucas, Adam Miner, Theresa Nguyen, Tim Althoff
| Challenge: | Psychotherapy can help people overcome negative thoughts by replacing them with a more hopeful "reframed thought" but clinician shortages and mental health stigma often limit access to therapy. |
| Approach: | They propose a framework of seven linguistic attributes that can be used to reframe a thought . they use a retrieval-enhanced in-context learning model to generate reframed thoughts . |
| Outcome: | The proposed model is based on a human-centered study of 600 situations, thoughts and reframes on 2,000 mental health websites. |
CHIME: LLM-Assisted Hierarchical Organization of Scientific Studies for Literature Review Support (2024.findings-acl)
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Chao-Chun Hsu, Erin Bransom, Jenna Sparks, Bailey Kuehl, Chenhao Tan, David Wadden, Lucy Wang, Aakanksha Naik
| Challenge: | Literature review requires researchers to synthesize a large amount of information. |
| Approach: | They propose to use LLMs to generate hierarchical organizations from a set of studies . they use a human-in-the-loop process to correct errors in LLM-generated hierarchies . |
| Outcome: | The proposed model improves assignment of studies to categories by 12.6 F1 points. |
Generating Scientific Claims for Zero-Shot Scientific Fact Checking (2022.acl-long)
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| Challenge: | Existing methods for scientific fact checking require domain expertise and time consuming. |
| Approach: | They propose a new supervised method for generating claims from scientific sentences and a novel method for negating claims. |
| Outcome: | The proposed method improves on existing methods on biomedical claims and negations. |
Language Models Hallucinate, but May Excel at Fact Verification (2024.naacl-long)
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| Challenge: | Recent advances in large language models (LLMs) have produced non-factual outputs . however, current LLMs suffer from the hallucination issue . |
| Approach: | They propose to use instruction-tuned LLMs to generate factual outputs . they find that FLAN-T5-11B performs best as a fact verifier . |
| Outcome: | The proposed method outperforms more capable LLMs like GPT3.5 and ChatGPT in the human evaluation. |
Entity, Relation, and Event Extraction with Contextualized Span Representations (D19-1)
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| Challenge: | Existing frameworks for named entity recognition, relation extraction, and event extraction can be easily adapted for new tasks or datasets. |
| Approach: | They propose a framework that enumerates, refins, and scores text spans to capture local (within-sentence) and global (cross-sentent) context. |
| Outcome: | The proposed framework achieves state-of-the-art results on four datasets from a variety of domains. |
HALoGEN: Fantastic LLM Hallucinations and Where to Find Them (2025.acl-long)
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| Challenge: | generative large language models produce hallucinations that are not aligned with world knowledge or input context. |
| Approach: | They propose a hallucination benchmark framework that measures hallucinism in large language models . they evaluate 150,000 generations from 14 language models and find they are riddled with hallucinos . |
| Outcome: | The proposed framework evaluates 150,000 generations from 14 language models. |
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers (2021.naacl-main)
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Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi
| Challenge: | COVID-19 has spawned a diverse body of scientific literature that is challenging to navigate . researchers are using automated tools to help find useful knowledge . |
| Approach: | They develop a schema to extract mechanism relations from scientific papers . their search engine, dataset and code are publicly available . |
| Outcome: | The proposed schema outperforms PubMed search in clinical trials. |
Fact or Fiction: Verifying Scientific Claims (2020.emnlp-main)
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David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi
| Challenge: | SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. |
| Approach: | They construct a dataset of 1.4K scientific claims paired with evidence-containing abstracts annotated with labels and rationales to test their system. |
| Outcome: | The proposed system can verify claims related to COVID-19 by identifying evidence from the CORD-19 corpus. |
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets (2024.findings-eacl)
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| Challenge: | Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. |
| Approach: | They conduct the first comprehensive analysis of large language models (LMs) for query or document expansion. |
| Outcome: | The proposed expansions improve retrieval performance for weaker models but harm stronger models. |
SciFact-Open: Towards open-domain scientific claim verification (2022.findings-emnlp)
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| Challenge: | Current scientific claim verification systems can achieve very strong performance on limited contexts, in some cases approaching human agreement. |
| Approach: | They propose to pool and annotate top predictions from four state-of-the-art scientific claim verification models to evaluate their performance against large corpora. |
| Outcome: | The proposed system performs well on a corpus of 500K scientific abstracts. |
MultiVerS: Improving scientific claim verification with weak supervision and full-document context (2022.findings-naacl)
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| Challenge: | a new approach to scientific claim verification uses a document-level fact-checking label to label scientific documents . a multitask approach combines a shared encoding of the claim and document context . |
| Approach: | They propose a system which predicts a fact-checking label and identifies rationales in a multitask fashion based on a shared encoding of the claim and full document context. |
| Outcome: | The proposed approach outperforms baselines on three scientific claim verification datasets . it can learn from instances annotated with a document-level fact-checking label, but lacking sentence-level rationales based on the datasets. |
KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used as conversational agents. |
| Approach: | They construct a dataset of knowledge-intensive writing instructions to evaluate LLMs' ability to follow user instructions. |
| Outcome: | The proposed model fails to integrate new information into an existing answer and perform precise and unambiguous edits. |
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature (2025.emnlp-main)
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David Wadden, Kejian Shi, Jacob Morrison, Alan Li, Aakanksha Naik, Shruti Singh, Nitzan Barzilay, Kyle Lo, Tom Hope, Luca Soldaini, Shannon Zejiang Shen, Doug Downey, Hannaneh Hajishirzi, Arman Cohan
| Challenge: | ScIRIFF is the only entirely expert-written instruction-following dataset for scientific literature understanding . it features complex instructions with long input contexts, detailed task descriptions, and structured outputs. |
| Approach: | They present a dataset of 137K instruction-following instances for training and evaluation . they finetuned large language models using a mix of general domain and ScIRIFF instructions . |
| Outcome: | The proposed dataset shows that on nine out-of-distribution held-out tasks, the model performs better than baselines trained on general domain instructions. |