Papers by Bangzheng Li
Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination? (2024.naacl-long)
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| Challenge: | Existing large language models (LLMs) suffer from hallucinations and unfaithful reasoning due to keyword/entity biases. |
| Approach: | They propose a new probing method and benchmark to quantify this phenomenon by using a keyword/entity biases-based probing technique called EUREQA. |
| Outcome: | The proposed method achieves 62% accuracy on multi-hop and complex QA benchmarks. |
Unified Semantic Typing with Meaningful Label Inference (2022.naacl-main)
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| Challenge: | Semantic typing aims at classifying tokens into semantic categories such as relations, entity types, and event types. |
| Approach: | They propose a unified framework for semantic typing that captures label semantics by projecting both inputs and labels into a joint semantic embedding space. |
| Outcome: | The proposed framework achieves strong performance across three semantic typing tasks. |
Red Teaming Language Models for Processing Contradictory Dialogues (2024.emnlp-main)
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| Challenge: | a recent study shows that language models are prone to self-contradiction during dialogues. |
| Approach: | They propose a red teaming framework that detects and attempts to explain dialogues, then modifies existing contradictory content using the explanation. |
| Outcome: | The proposed task improves the ability to detect contradictory dialogues and provides valid explanations. |
Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking (2024.findings-naacl)
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| Challenge: | Large language models (LLMs) have demonstrated increasing power, but they also have vulnerabilities. |
| Approach: | They propose a black-box attack that targets the cognitive structure and processes of large language models (LLMs) they propose defending cognitive overload attacks from three perspectives. |
| Outcome: | The proposed attack is a black-box attack with no need for knowledge of model architecture or access to model weights. |
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)
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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych
| Challenge: | a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications . |
| Approach: | a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19. |
| Outcome: | a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing . |
Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference (2022.tacl-1)
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| Challenge: | Existing methods for ultra-fine entity typing fail to capture type semantics because of the large number of types and the scarcity of data per type. |
| Approach: | They propose a method that formulates entity typing as a natural language inference problem . they use indirect supervision from NLI to infer type information as textual hypotheses . |
| Outcome: | The proposed method achieves state-of-the-art performance on the ultra-fine entity typing task with limited training data. |
Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing (2022.emnlp-main)
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| Challenge: | Existing entity typing models are subject to spurious correlations due to shortcuts and biased training. |
| Approach: | They propose a method to augment existing model biases by combining spurious correlations with debiasedcounterparts to improve generalization. |
| Outcome: | The proposed method improves generalization of different entity typing models on the original and debiased test sets. |
Affective and Dynamic Beam Search for Story Generation (2023.findings-emnlp)
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| Challenge: | AffGen introduces ‘intriguing twists’ in narratives by employing two novel techniques—Dynamic Beam Sizing and Affective Reranking. |
| Approach: | They propose to use dynamic beam sizing and affective reranking to generate interesting stories using two novel techniques. |
| Outcome: | The proposed method outperforms baseline models in generating affectively charged and interesting narratives. |