Papers by Junlang Qian
Beyond the Next Token: Towards Prompt-Robust Zero-Shot Classification via Efficient Multi-Token Prediction (2025.naacl-long)
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| Challenge: | Existing methods for zero-shot text classification lack prompt engineering due to prompt brittleness . however, these methods are not effective for zero shot text classifications . |
| Approach: | They propose a method that predicts token probabilities across multiple positions and simulates comprehensive sampling of generation paths in a single run of a language model. |
| Outcome: | The proposed approach improves accuracy and reduces standard deviation by 98% . it maintains comparable performance even without a prompt, reducing the need for prompt engineering . |
Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context Learning (2025.naacl-long)
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| Challenge: | Effective organization of in-context learning (ICL) demonstrations is key to improving the quality of large language models (LLMs). |
| Approach: | They propose a logit separability-based method that integrates multiple class-related words into each sample-label pair to improve LLM understanding. |
| Outcome: | The proposed method improves ICL performance by providing clearer instructions and richer label information. |
EDEntail: An Entailment-based Few-shot Text Classification with Extensional Definition (2024.findings-naacl)
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| Challenge: | Existing methods for few-shot text classification use either class labels or intensional definitions of class labels for label semantics expression. |
| Approach: | They propose a method that employs extensional definition of class labels in hypotheses and then order and format them into a sequence to form hypothese . |
| Outcome: | The proposed method surpasses supervised-learning methods and prompt-based methods on five classification datasets and is comparable to state-of-the-art models. |
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction (2024.acl-long)
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| Challenge: | In-context learning (ICL) is an emerging ability of large-scale labeled data for document-level event argument extraction (EAE). |
| Approach: | They propose an explicit heuristic-driven demonstration construction approach that emphasizes task heurs in document-level event argument extraction tasks. |
| Outcome: | The proposed method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets. |