Nikita Haduong


pdf bib
Risks and NLP Design: A Case Study on Procedural Document QA
Nikita Haduong | Alice Gao | Noah A. Smith
Findings of the Association for Computational Linguistics: ACL 2023

As NLP systems are increasingly deployed at scale, concerns about their potential negative impacts have attracted the attention of the research community, yet discussions of risk have mostly been at an abstract level and focused on generic AI or NLP applications. We argue that clearer assessments of risks and harms to users—and concrete strategies to mitigate them—will be possible when we specialize the analysis to more concrete applications and their plausible users. As an illustration, this paper is grounded in cooking recipe procedural document question answering (ProcDocQA), where there are well-defined risks to users such as injuries or allergic reactions. Our case study shows that an existing language model, applied in “zero-shot” mode, quantitatively answers real-world questions about recipes as well or better than the humans who have answered the questions on the web. Using a novel questionnaire informed by theoretical work on AI risk, we conduct a risk-oriented error analysis that could then inform the design of a future system to be deployed with lower risk of harm and better performance.


pdf bib
All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text
Elizabeth Clark | Tal August | Sofia Serrano | Nikita Haduong | Suchin Gururangan | Noah A. Smith
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)

Human evaluations are typically considered the gold standard in natural language generation, but as models’ fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts’ ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recipes). We find that, without training, evaluators distinguished between GPT3- and human-authored text at random chance level. We explore three approaches for quickly training evaluators to better identify GPT3-authored text (detailed instructions, annotated examples, and paired examples) and find that while evaluators’ accuracy improved up to 55%, it did not significantly improve across the three domains. Given the inconsistent results across text domains and the often contradictory reasons evaluators gave for their judgments, we examine the role untrained human evaluations play in NLG evaluation and provide recommendations to NLG researchers for improving human evaluations of text generated from state-of-the-art models.


pdf bib
IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter
Can Liu | Wen Li | Bradford Demarest | Yue Chen | Sara Couture | Daniel Dakota | Nikita Haduong | Noah Kaufman | Andrew Lamont | Manan Pancholi | Kenneth Steimel | Sandra Kübler
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)