Papers by Nick McKenna
Sources of Hallucination by Large Language Models on Inference Tasks (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI) |
| Approach: | They propose to use LLMs to probe their behavior using controlled experiments. |
| Outcome: | The proposed models perform significantly worse on NLI test samples which do not conform to these biases than those which do. |
Multivalent Entailment Graphs for Question Answering (2021.emnlp-main)
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Nick McKenna, Liane Guillou, Mohammad Javad Hosseini, Sander Bijl de Vroe, Mark Johnson, Mark Steedman
| Challenge: | a recent study shows that drawing inferences between open domain predicates is a necessity for true language understanding. |
| Approach: | They propose to reinterpret the Distributional Inclusion Hypothesis to model entailment between predicates of different valencies. |
| Outcome: | The proposed graphs are more useful than using the same valency evidence, the authors show . they show that drawing on evidence across valencies answers more questions than using only the same evidence. |
Learning Negation Scope from Syntactic Structure (2020.starsem-1)
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| Challenge: | a semi-supervised model learns the semantics of negation purely through syntactic analysis . Negation is a semantic phenomenon in natural language which varies significantly . |
| Approach: | They propose a semi-supervised model which learns negation semantics purely through syntactic analysis. |
| Outcome: | The proposed model achieves state-of-the-art on a Negation Scope Detection task without identifying individual words or extracting features beyond syntax. |