Papers by Seth Ebner
Multi-Sentence Argument Linking (2020.acl-main)
Copied to clipboard
| Challenge: | Existing datasets for cross-sentence linking are small, resulting in a lack of a model for argument linking. |
| Approach: | They propose a document-level model for finding argument spans that fill an event’s roles by combining semantic role labeling and coreference resolution. |
| Outcome: | The proposed model is able to connect arguments in sentence-level role labeling and coreference resolution on 9,124 annotated events across 139 types. |
Core: Robust Factual Precision with Informative Sub-Claim Identification (2025.findings-acl)
Copied to clipboard
Zhengping Jiang, Jingyu Zhang, Nathaniel Weir, Seth Ebner, Miriam Wanner, Kate Sanders, Daniel Khashabi, Anqi Liu, Benjamin Van Durme
| Challenge: | Using the Decompose-Then-Verify framework, such as FActScore, can be manipulated by adding obvious or repetitive subclaims to artificially inflate scores. |
| Approach: | They propose a decomposition-based tool called Core to filter subclaims based on their uniqueness and informativeness. |
| Outcome: | The proposed evaluation framework supports easy and modular use of Core and various decomposition strategies. |
A Closer Look at Claim Decomposition (2024.starsem-1)
Copied to clipboard
| Challenge: | Recent work uses claim decomposition to determine how well supported a claim is for applications in factual precision of generated text, entailment of human generated text and claim verification. |
| Approach: | They propose an LLM-based approach to generating decompositions inspired by Bertrand Russell’s theory of logical atomism and neo-Davidsonian semantics and demonstrate its improved decomposing quality over previous methods. |
| Outcome: | The proposed method improves on the FActScore and a Bertrand Russell-inspired approach to generating decompositions inspired by neo-Davidsonian semantics and improves decomposability quality. |
Language Model Probabilities are Not Calibrated in Numeric Contexts (2025.acl-long)
Copied to clipboard
Charles Lovering, Michael Krumdick, Viet Dac Lai, Varshini Reddy, Seth Ebner, Nilesh Kumar, Rik Koncel-Kedziorski, Chris Tanner
| Challenge: | Using language model outputs, we find that even in simple settings, the best LMs (1) are poorly calibrated and (2) have systematic biases. |
| Approach: | They argue that language model outputs should capture natural distributions over multiple options within their textual contexts. |
| Outcome: | The proposed model outputs are calibrated to the numeric content of their contexts. |
Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning (D19-61)
Copied to clipboard
| Challenge: | Existing approaches to multi-task learning take advantage of transfer among tasks . generative reconstruction of the observations is not included in the standard framework . |
| Approach: | They propose to use a syntactically-oblivious pooling encoder and pre-trained word embeddings to improve sentence-level representations. |
| Outcome: | The proposed techniques yield similar performance on a universe of task combinations while reducing training time and model size. |
1,729 vs. 1729: The Effect of Scripts and Formats on LLM Numeracy (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have impressive proficiency in basic arithmetic, but little attention has been given to how they perform when numerical expressions deviate from the prevailing conventions present in their training corpora. |
| Approach: | They investigate numerical reasoning across a wide range of numeral scripts and formats . they show that LLM accuracy drops substantially when numerical inputs are rendered in underrepresented scripts or formats despite the underlying mathematical reasoning being identical . |
| Outcome: | The proposed methods can narrow the gap between LLMs and human models when they deviate from prevailing numerical conventions. |
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction (2021.emnlp-main)
Copied to clipboard
Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme
| Challenge: | Zero-shot cross-lingual information extraction (IE) is a technique for training data in a source language but not in . |
| Approach: | They explore techniques including data projection and self-training to improve zero-shot cross-lingual information extraction (IE) IE is a construction of an IE model for some target language given existing annotations exclusively in English. |
| Outcome: | The proposed techniques show that they perform better than any single strategy. |
On Finding Inconsistencies in Documents (2026.findings-acl)
Copied to clipboard
Charles Lovering, Seth Ebner, Brandon Smock, Michael Krumdick, Muhammad Saad Rabbani, Ahmed Muhammad, Varshini Reddy, Chris Tanner
| Challenge: | Language models can be used to quickly and easily detect inconsistencies in documents . |
| Approach: | They propose a benchmark to measure language models' ability to detect inconsistencies in documents . they use a document with an inconsistent inserted manually by a domain expert . |
| Outcome: | The best-performing model recovered 64% of the inserted inconsistencies on 50 arXiv papers and found that the original authors had already found inconsistent inconsistances. |