Papers by Elizabeth Clark
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation (2023.emnlp-main)
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Elizabeth Clark, Shruti Rijhwani, Sebastian Gehrmann, Joshua Maynez, Roee Aharoni, Vitaly Nikolaev, Thibault Sellam, Aditya Siddhant, Dipanjan Das, Ankur Parikh
| Challenge: | evaluating the quality of generated text is a difficult problem for large language models. |
| Approach: | They propose a dataset for multilingual, multifaceted summarization evaluation. |
| Outcome: | The proposed dataset can be used to train multilingual summarization systems . it shows that the dataset performs well on the out-of-domain meta-evaluation benchmarks TRUE and mFACE . |
Help Me Write a Story: Evaluating LLMs’ Ability to Generate Writing Feedback (2025.acl-long)
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| Challenge: | Current models provide specific and mostly accurate writing feedback, but they fail to identify the biggest writing issue in the story and to correctly decide when to offer critical vs. positive feedback. |
| Approach: | They propose a task that corrupts 1,300 stories to intentionally introduce writing issues to study model performance. |
| Outcome: | The proposed model performs well in a controlled task with human and automatic evaluation metrics. |
Multilingual Summarization with Factual Consistency Evaluation (2023.findings-acl)
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| Challenge: | Abstractive summarization models generate factually inconsistent summaries, reducing their utility for real-world applications. |
| Approach: | They propose to use data filtering and controlled generation to detect hallucinations in machine generated summaries. |
| Outcome: | The proposed models detect factual inconsistencies in machine generated summaries, but they focus on English only. |
Counterfactual Story Reasoning and Generation (D19-1)
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| Challenge: | a desired property of AI systems is counterfactual reasoning: ability to predict causal changes in future events. |
| Approach: | They propose to rewrite a short story and a counterfactual event to make it compatible with the given counterfact. |
| Outcome: | The proposed task requires deep understanding of causal narrative chains and counterfactual invariance . the proposed dataset includes 81,407 counterfact "branches" without a rewritten storyline . |
Sentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts (P19-1)
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| Challenge: | Existing automatic metrics for evaluating text are expensive and time-consuming. |
| Approach: | They propose automatic metrics that evaluate text in a continuous space using word and sentence embeddings. |
| Outcome: | The proposed method outperforms ROUGE on machine-generated summaries and human-authored essays on human-generated texts. |
A Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization (2023.acl-long)
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Lining Zhang, Simon Mille, Yufang Hou, Daniel Deutsch, Elizabeth Clark, Yixin Liu, Saad Mahamood, Sebastian Gehrmann, Miruna Clinciu, Khyathi Raghavi Chandu, João Sedoc
| Challenge: | Using crowdsourcing, it is difficult to obtain high-quality annotations for difficult tasks. |
| Approach: | They propose a recruitment pipeline to recruit high-quality Amazon Mechanical Turk workers . they filter out subpar workers before they carry out the evaluations . |
| Outcome: | The proposed method can filter out subpar workers before they carry out evaluations and obtain high-agreement annotations with similar constraints on resources. |
TuringAdvice: A Generative and Dynamic Evaluation of Language Use (2021.naacl-main)
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| Challenge: | Empirical results show that today’s language models struggle at TuringAdvice . language models are getting ever-larger, and are being trained on ever-increasing quantities of text . |
| Approach: | They propose a task task that requires models to generate helpful advice in natural language. |
| Outcome: | The proposed model outperforms even multibillion parameter models on 600k in-domain training examples. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts (2024.emnlp-main)
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| Challenge: | Simplifying the entire text makes it understandable but sometimes removes important details. |
| Approach: | They propose a simplification task for rewriting text to help readers comprehend text containing unfamiliar concepts and introduce a dataset of 22k definitions from 13 academic domains paired with a difficult concept within each definition. |
| Outcome: | The proposed model outperforms open-source and commercial models on the task and human judges prefer explanations over simplifications of the difficult concept. |
Don’t Take This Out of Context!: On the Need for Contextual Models and Evaluations for Stylistic Rewriting (2023.emnlp-main)
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| Challenge: | Existing stylistic text rewriting methods ignore the context of the text, causing generic, incoherent, and generic outputs. |
| Approach: | They propose a contextual evaluation metric that integrates preceding context into stylistic text rewriting. |
| Outcome: | The proposed metric integrates the preceding textual context into rewriting and evaluation stages . human preferences are better reflected by the proposed criterio and other metrics . |
All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text (2021.acl-long)
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| Challenge: | evaluators distinguish between human- and machine-authored text in three domains without training . evals' accuracy improved up to 55%, but it did not significantly improve across the three domain. |
| Approach: | They examine the role untrained human evaluations play in NLG evaluation and propose ways to improve their evaluations. |
| Outcome: | The evaluators distinguished between human- and machine-authored text at random chance level without training, but their accuracy did not improve across the three domains. |
Neural Text Generation in Stories Using Entity Representations as Context (N18-1)
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| Challenge: | Existing models of text generation that explicitly represent entities are based on the use of words and entities. |
| Approach: | They propose a neural model that explicitly represents entities mentioned in the text . they use vectors that are updated as the text proceeds to improve automatic evaluations . |
| Outcome: | The proposed model improves mention generation, sentence selection, and sentence generation. |
Choose Your Own Adventure: Paired Suggestions in Collaborative Writing for Evaluating Story Generation Models (2021.naacl-main)
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| Challenge: | Comparative evaluations have been shown to produce more reliable and consistent results than Likert scale ratings. |
| Approach: | They propose a collaborative writing setup where two models generate suggestions to people as they write a short story and then ask them to choose which model's suggestions they prefer. |
| Outcome: | The proposed model performs better in cases where the differences in generation methods are small (nucleus vs. top-k sampling) and large (GPT2 v. Fusion models). |
Sounding Board: A User-Centric and Content-Driven Social Chatbot (N18-5)
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Hao Fang, Hao Cheng, Maarten Sap, Elizabeth Clark, Ari Holtzman, Yejin Choi, Noah A. Smith, Mari Ostendorf
| Challenge: | Sounding Board is a social chatbot that can hold a coherent conversation with humans . the system is user-centric in that users can control the topic of conversation, while the system adapts to the user's needs. |
| Approach: | They present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize. |
| Outcome: | The system is user-centric in that users can control the topic of conversation, while the system adapts to the user's needs. |
Dialect-robust Evaluation of Generated Text (2023.acl-long)
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Jiao Sun, Thibault Sellam, Elizabeth Clark, Tu Vu, Timothy Dozat, Dan Garrette, Aditya Siddhant, Jacob Eisenstein, Sebastian Gehrmann
| Challenge: | Existing evaluation metrics that are not robust to dialect variation are difficult to measure for many groups of users and can penalize systems for producing text in lower-resource dialects. |
| Approach: | They propose a dialect-robust evaluation metric that produces the same score for system outputs that share the same semantics but are expressed in different dialects. |
| Outcome: | The proposed method significantly improves dialect robustness while preserving the correlation between automated metrics and human ratings. |