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Zhenlin Su

3 accepted papers

2025

Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings

EMNLP 2025

This work stems from an observed limitation of text encoders: embeddings may not be able to recognize fine-grained entities or events within encoded semantics, resulting in failed retrieval even in simple cases. To examine such behaviors, we first introduce a new evaluation dataset, CapRetrieval, in

2024

Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy

EMNLP 2024finding

Factual inconsistencies pose a significant hurdle for the faithful summarization by generative models. While a major direction to enhance inconsistency detection is to derive stronger Natural Language Inference (NLI) models, we propose an orthogonal aspect that underscores the importance of incorpor…

2024

SIG: Speaker Identification in Literature via Prompt-Based Generation

AAAI 2024technical

Identifying speakers of quotations in narratives is an important task in literary analysis, with challenging scenarios including the out-of-domain inference for unseen speakers, and non-explicit cases where there are no speaker mentions in surrounding context. In this work, we propose a simple and…