Papers by Pingjun Hong
Evaluating Large Language Models for Cross-Lingual Retrieval (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have been evaluated as second-stage reranking models for monolingual IR, but a systematic comparison is lacking for cross-lingual reranked IR. |
| Approach: | They propose to use machine translation to evaluate rerankers in cross-lingual IR . they find that LLMs perform better than LLM-based reranked models . |
| Outcome: | The proposed model improves cross-lingual IR but relies on machine translation for the first stage. |
Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations (2026.findings-acl)
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| Challenge: | Natural Language Inference (NLI) datasets often exhibit label variation. |
| Approach: | They extend LiTEx taxonomy to two NLI datasets and jointly analyze label variation and label variation. |
| Outcome: | The proposed model combines explanations as a lens to analyze variation in NLI annotations and examine individual differences in reasoning. |
LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference (2025.emnlp-main)
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| Challenge: | Existing evidence of human label variation in Natural Language Inference (NLI) however, within-label variation is an additional challenge. |
| Approach: | They propose a linguistically-informed taxonomy for categorizing free-text explanations in English that captures different reasoning strategies behind NLI explanations with a particular focus on within-label variation. |
| Outcome: | The proposed taxonomy can be used to classify explanations in English using a linguistically-informed taxonomies. |