Papers with uncertainty
CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China (2026.acl-long)
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| Challenge: | Adaptive policy communication is a theory of governance in large, decentralized organizations where leaders exercise influence rather than precise control by combining clear and ambiguous instructions to calibrate discipline and flexibility. |
| Approach: | They propose an expert-directed annotation method that integrates codebook design, structured training, a two-step workflow, and LLM-based scaling. |
| Outcome: | The proposed method achieves a Fleiss’ kappa of 0.86 on directive labels, indicating high reliability. |
FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion (2024.findings-acl)
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| Challenge: | Existing work models taxonomy concepts as vectors or geometric objects, but fuzzy sets are efficient for concept modeling. |
| Approach: | They propose a set representation learning task based on fuzzy set approximation . they demonstrate remarkable improvements in taxonomy expansion using FUSE . |
| Outcome: | The proposed framework improves taxonomy expansion performance by 23% over baselines. |
I Beg to Differ: A study of constructive disagreement in online conversations (2021.eacl-main)
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| Challenge: | Disagreements are pervasive in human communication. |
| Approach: | They construct a corpus of Wikipedia Talk page conversations that contain content disputes and define the task of predicting whether disagreements will be escalated to mediation by a moderator. |
| Outcome: | The proposed model outperforms feature-based models in predicting whether disagreements will escalate to mediation by a moderator. |
On the Idiosyncrasies of the Mandarin Chinese Classifier System (N19-1)
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| Challenge: | idiosyncrasies of the Chinese classifier system have been studied, but little work has been done to quantify them with statistical methods. |
| Approach: | They propose an information-theoretic approach to measuring idiosyncrasies in Mandarin Chinese by calculating the mutual information between the distribution over classifiers and distributions over other linguistic quantities. |
| Outcome: | The proposed method reduces uncertainty in Mandarin Chinese classifiers by knowing semantic information about nouns that they modify. |
Speculative Verification: Exploiting Information Gain for Speculative Decoding (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are used for many applications but their size and computational cost make inference serving a significant challenge. |
| Approach: | They propose an efficient augmentation to Speculative Decoding (SD) that predicts speculation accuracy and dynamically adapts the verification length to maximize throughput. |
| Outcome: | The proposed model reduces wasted verification on rejected tokens and improves decoding efficiency. |