Papers by Jiaming Shang
PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations (2026.acl-long)
Copied to clipboard
Yuhe Wu, Guangyu Wang, Yuran Chen, Jiatong Zhang, Yutong Zhang, Yujie Chen, Jiaming Shang, Guang Zhang, Zhuang Liu
| Challenge: | Existing benchmarks for hallucination evaluation rely on mixed queries and posterior evaluation, which quantifies hallucinosity severity but offers limited insight into where and why they occur. |
| Approach: | They propose a controlled benchmark that disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors. |
| Outcome: | The proposed model disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors. |
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery (2020.emnlp-main)
Copied to clipboard
| Challenge: | Entity set expansion and synonym discovery are two critical NLP tasks that are often performed separately, without exploring their interdependencies. |
| Approach: | They propose a framework that enables two tasks to mutually enhance each other by including popular entities’ infrequent synonyms into the set, which boosts set expansion recall. |
| Outcome: | The proposed framework can be used to enhance two NLP tasks by including popular entities’ infrequent synonyms into the set, which boosts set expansion recall. |
Empower Entity Set Expansion via Language Model Probing (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for expanding seed entities with new entities belong to the same semantic class are difficult to implement and can lead to accumulative errors. |
| Approach: | They propose an iterative set expansion framework that leverages automatically generated class names to address the semantic drift issue. |
| Outcome: | The proposed framework generates high-quality class names and outperforms state-of-the-art methods significantly. |
TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names (2021.naacl-main)
Copied to clipboard
| Challenge: | Hierarchical multi-label text classification (HMTC) aims to assign each text document to a set of relevant classes from a taxonomy. |
| Approach: | They propose to conduct HMTC based on only class surface names as supervision signals to mimic human experts. |
| Outcome: | The proposed framework outperforms the best existing method by 25% on two challenging datasets. |
Phrase-aware Unsupervised Constituency Parsing (2022.acl-long)
Copied to clipboard
| Challenge: | Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task. |
| Approach: | They propose to regularize the parser with phrases extracted by an unsupervised phrase tagger to help the LM model quickly manage low-level structures. |
| Outcome: | The proposed method improves the identification of high-level structures using phrase-guided masking. |