Papers with general NLP tasks
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding (2023.eacl-main)
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| Challenge: | State-of-the-art entity tracking approaches either design complicated model architectures or rely on task-specific pre-training to achieve good results. |
| Approach: | They propose a multi-task learning-enabled entity tracking approach that utilizes knowledge gained from general domain tasks to improve entity tracking. |
| Outcome: | The proposed approach achieves state-of-the-art on two popular entity tracking datasets, even though it does not require any task-specific architecture design or pre-training. |
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. |
QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs (2026.findings-acl)
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| Challenge: | Recent studies have shown that large language models (LLMs) can be effective for correcting factual inaccuracies but can still suffer from hallucinations. |
| Approach: | They propose a queue-based self-correction framework that addresses parameter bias during sequential model editing. |
| Outcome: | The proposed framework outperforms baseline models while maintaining competitive performance in single-turn editing. |
Delta Embedding Learning (P19-1)
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| Challenge: | Unsupervised word embeddings have limitations to the semantics of words and inadequate fine-tuning of embedded word can lead to suboptimal performance. |
| Approach: | They propose a method that optimizes word embeddings by regularizing them incrementally to ensure they are tuned in an incremental way. |
| Outcome: | The proposed method improves performance on various NLP tasks and shows that it absorbs semantic information without "forging" |