Self-Consistency, a widely-used decoding strategy, significantly boosts the reasoning capabilities of Large Language Models (LLMs). However, it depends on the plurality voting rule, which focuses on the most frequent answer while overlooking all other minority responses. These inconsistent minority views often illuminate areas of uncertainty within the model’s generation process. To address this limitation, we present Mirror-Consistency, an enhancement of the standard Self-Consistency approach. Our method incorporates a ‘reflective mirror’ into the self-ensemble decoding process and enables LLMs to critically examine inconsistencies among multiple generations. Additionally, just as humans use the mirror to better understand themselves, we propose using Mirror-Consistency to enhance the sample-based confidence calibration methods, which helps to mitigate issues of overconfidence. Our experimental results demonstrate that Mirror-Consistency yields superior performance in both reasoning accuracy and confidence calibration compared to Self-Consistency.

In low-resource Named Entity Recognition (NER) scenarios, only a limited quantity of strongly labeled data is available, while a vast amount of weakly labeled data can be easily acquired through distant supervision. However, weakly labeled data may fail to improve the model performance or even harm it due to the inevitable noise. While training on noisy data, only certain parameters are essential for model learning, termed safe parameters, whereas the other parameters tend to fit noise. In this paper, we propose a noise-robust learning framework where safe parameters can be identified with guidance from the small set of strongly labeled data, and non-safe parameters are suppressed during training on weakly labeled data for better generalization. Our method can effectively mitigate the impact of noise in weakly labeled data, and it can be easily integrated with data level noise-robust learning methods for NER. We conduct extensive experiments on multiple datasets and the results show that our approach outperforms the state-of-the-art methods.

Distantly-supervised named entity recognition (NER) aims at training networks with distantly-labeled data, which is automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base. Distant supervision may induce incomplete and noisy labels, so recent state-of-the-art methods employ sample selection mechanism to separate clean data from noisy data based on the model’s prediction scores. However, they ignore the noise distribution change caused by data selection, and they simply excludes noisy data during training, resulting in information loss. We propose to (1) use a dynamic loss function to better adapt to the changing noise during the training process, and (2) incorporate token level contrastive learning to fully utilize the noisy data as well as facilitate feature learning without relying on labels. Our method achieves superior performance on three benchmark datasets, outperforming existing distantly supervised NER models by significant margins.

Zero-Resource Cross-Lingual Named Entity Recognition aims at training an NER model of the target language using only labeled source language data and unlabeled target language data. Existing methods are mainly divided into three categories: model transfer based, data transfer based and knowledge transfer based. Each method has its own disadvantages, and combining more than one of them often leads to better performance. However, the performance of data transfer based methods is often limited by inevitable noise in the translation process. To handle the problem, we propose a framework named TransAdv to mitigate lexical and syntactic errors of word-by-word translated data, better utilizing the data by multi-level adversarial learning and multi-model knowledge distillation. Extensive experiments are conducted over 6 target languages with English as the source language, and the results show that TransAdv achieves competitive performance to the state-of-the-art models.

Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity. Existing methods are poor at detecting complicated relations between aspects and opinions as well as classifying multiple sentiment polarities in a sentence. Detecting unclear boundaries of multi-word aspects and opinions is also a challenge. In this paper, we propose a Multi-Task Dual-Tree Network (MTDTN) to address these issues. We employ a constituency tree and a modified dependency tree in two sub-tasks of Aspect Opinion Co-Extraction (AOCE) and ASTE, respectively. To enhance the information interaction between the two sub-tasks, we further design a Transition-Based Inference Strategy (TBIS) that transfers the boundary information from tags of AOCE to ASTE through a transition matrix. Extensive experiments are conducted on four popular datasets, and the results show the effectiveness of our model.