Papers by Anthony Hartshorn
Assessing Robustness of Text Classification through Maximal Safe Radius Computation (2020.findings-emnlp)
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| Challenge: | Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction. |
| Approach: | They propose to provide a measure of robustness against word substitutions by computing a safe radius for a given input text. |
| Outcome: | The proposed methods are compared with LIME and CNN-Cert and show that they perform well on sentiment analysis and news classification models. |
HalluLens: LLM Hallucination Benchmark (2025.acl-long)
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Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, Pascale Fung
| Challenge: | Large language models (LLMs) generate responses that deviate from user input or training data, a phenomenon known as "hallucination" . |
| Approach: | They propose a hallucination benchmark HalluLens that includes both extrinsic and intrinsic evaluation tasks to distinguish between extrindic and intrinsic hallucines. |
| Outcome: | The proposed framework disentangles LLM hallucination from "factuality" and distinguishes between extrinsic and intrinsic hallucines to promote consistency and facilitate research. |
Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations (2025.emnlp-main)
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Ziwei Ji, Lei Yu, Yeskendir Koishekenov, Yejin Bang, Anthony Hartshorn, Alan Schelten, Cheng Zhang, Pascale Fung, Nicola Cancedda
| Challenge: | LLMs often use assertive language when making false claims, resulting in harm and loss of trust. |
| Approach: | They find that a mismatch between semantic and verbal uncertainty is a better predictor of hallucinations than semantic uncertainty alone. |
| Outcome: | a new study shows that mismatch between semantic and verbal uncertainty is better predictor of hallucinations than semantic uncertainty alone. |