Papers with heuristic
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models (2024.acl-long)
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Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, Kaidi Xu
| Challenge: | Large Language Models (LLMs) show promising results in language generation but often “hallucinate”, making their outputs less reliable. |
| Approach: | They propose to shift attention to more relevant components at token- and sentence-levels for better UQ. |
| Outcome: | The proposed approach improves the performance of a range of popular “off-the-shelf” LLMs with model sizes extending up to 33B parameters. |
DirectProbe: Studying Representations without Classifiers (2021.naacl-main)
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| Challenge: | Existing approaches for probing opaque representations often use training classifiers and use the accuracy, mutual information, or complexity as a proxy for the representation’s goodness. |
| Approach: | They propose a heuristic that directly studies the geometry of a representation by building upon the notion of 'version space' they argue that doing so can be unreliable because different representations may need different classifiers . |
| Outcome: | Experiments with linguistic tasks and contextualized embeddings show that even without training classifiers, DirectProbe can shine lights on how an embeddable space represents labels and anticipate the classifier performance for the representation. |
Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models (2022.emnlp-main)
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| Challenge: | Cognitive psychologists have documented that humans use cognitive heuristics to make quick decisions while expending less effort. |
| Approach: | They propose tracking annotator heuristic traces where they measure low-effort annotation strategies that could indicate usage of various cognitive heurs. |
| Outcome: | The proposed tracking annotator heuristic traces shows that annotators are using multiple cognitive heurs based on psychological tests. |
A Root of a Problem: Optimizing Single-Root Dependency Parsing (2021.emnlp-main)
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| Challenge: | Graph-based dependency parsers can be improved without compromising on accuracy or accuracy. |
| Approach: | They propose two approaches to single-root dependency parsing that yield speed ups . they show that one approach is fully correct and finds the optimal dependency tree . |
| Outcome: | The proposed approach finds the optimal dependency tree without loss of accuracy or optimality. |
Safeguarding LLM Fine-tuning via Push-Pull Distributional Alignment (2026.acl-long)
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| Challenge: | Existing safety defenses for large language models fail to explicitly repel harmful patterns . Optimal transport (SOT) allows for safe fine-tuning without sacrificing safety . |
| Approach: | They propose a framework that reframes safe fine-tuning from instance-level filtering challenge to distribution-level alignment task grounded in Optimal Transport. |
| Outcome: | a new framework improves safety of large language models while maintaining competitive performance . the proposed framework reduces the risk of errors and improves model performance compared to baselines . |