4 papers · 1 filter
Do LLM Self-Explanations Help Users Predict Model Behavior? Evaluating Counterfactual Simulatability with Pragmatic Perturbations
Pingjun Hong, Benjamin Roth
Large Language Models (LLMs) can produce verbalized self-explanations, yet prior studies suggest that such rationales may not reliably reflect the model's true decision process. We…
Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations
Pingjun Hong, Beiduo Chen, Siyao Peng +3
Natural Language Inference (NLI) datasets often exhibit human label variation. To better understand these variations, explanation-based approaches analyze the underlying reasoning…
Evaluating Large Language Models for Cross-Lingual Retrieval
Longfei Zuo, Pingjun Hong, Oliver Kraus +2
Multi-stage information retrieval (IR) has become a widely-adopted paradigm in search. While Large Language Models (LLMs) have been extensively evaluated as second-stage reranking…
LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference
Pingjun Hong, Beiduo Chen, Siyao Peng +2
There is increasing evidence of Human Label Variation (HLV) in Natural Language Inference (NLI), where annotators assign different labels to the same premise-hypothesis pair. Howev…