7 citations · 9 across the 8 of their papers we have counts for
8 papers
A Robust Evaluation of Probe Robustness: Lessons for Reliable OOD Uncertainty Quantification
Joe Stacey, Hadas Orgad, Kentaro Inui +2
Recent work has shown that the hidden states of large language models contain signals useful for uncertainty estimation, motivating a growing interest in efficient probe-based appr…
AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios
Lisa Alazraki, Lihu Chen, Ana Brassard +3
Large Language Models (LLMs) have achieved high accuracy on complex commonsense and mathematical problems that involve the composition of multiple reasoning steps. However, current…
Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection
Joe Stacey, Lisa Alazraki, Aran Ubhi +3
We investigate the robustness of fine-tuned Large Language Models (LLMs) for the task of Natural Language Inference (NLI), finding that the in-distribution gains from fine-tuning c…
Atomic Inference for NLI with Generated Facts as Atoms
Joe Stacey, Pasquale Minervini, Haim Dubossarsky +2
With recent advances, neural models can achieve human-level performance on various natural language tasks. However, there are no guarantees that any explanations from these models…
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation
Joe Stacey, Marek Rei
Knowledge distillation optimises a smaller student model to behave similarly to a larger teacher model, retaining some of the performance benefits. While this method can improve re…
Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models
Joe Stacey, Pasquale Minervini, Haim Dubossarsky +1
Current Natural Language Inference (NLI) models achieve impressive results, sometimes outperforming humans when evaluating on in-distribution test sets. However, as these models ar…
Supervising Model Attention with Human Explanations for Robust Natural Language Inference
Joe Stacey, Yonatan Belinkov, Marek Rei
Natural Language Inference (NLI) models are known to learn from biases and artefacts within their training data, impacting how well they generalise to other unseen datasets. Existi…
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
Joe Stacey, Pasquale Minervini, Haim Dubossarsky +2
Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment cl…