Papers by Lingbo Mo
Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction (2022.findings-acl)
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| Challenge: | Existing studies on semantic parsing focus on mapping a natural-language utterance to a logical form (LF) but natural language may contain ambiguity and variability, making this challenge difficult. |
| Approach: | They propose an interactive semantic parsing framework that explains the predicted LF step by step in natural language and enables the user to make corrections through natural-language feedback for individual steps. |
| Outcome: | The proposed framework improves parsing accuracy and transparency in a crowdsourced dialogue dataset. |
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities (2024.naacl-long)
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| Challenge: | Rapid progress in open-source Large Language Models (LLMs) is driving AI development, but lacks sufficient trustworthiness to detect and mitigate adversarial demonstrations. |
| Approach: | They propose an extended Chain of Utterances-based (CoU) prompting strategy to attack open-source LLMs. |
| Outcome: | The proposed attack strategy is based on malicious demonstrations and toxicity tests on open-source models. |
A Multi-Aspect Framework for Counter Narrative Evaluation using Large Language Models (2024.naacl-short)
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| Challenge: | Existing methods for counter narrative evaluation lack alignment with human judgment as they rely on superficial reference comparisons instead of incorporating key aspects of counter narrative quality as evaluation criteria. |
| Approach: | They propose to use 5 defined aspects to generate counter narrative candidates using human-annotated scores and feedback from counter narrative specialized NGOs to assess their effectiveness. |
| Outcome: | The proposed evaluation framework outperforms existing metrics and achieves strong alignment to human-annotated scores and feedback. |