Language Task Performance Research Articles

Exploring the intricacies of human memory and its analogous representation in ChatGPT

Human memory and ChatGPT both rely on associations and patterns to generate contextually relevant responses. We explore how they work in tandem. Both use associations to activate related information when prompted. Memory forms generic representations that become precise with added details, similar to ChatGPT's responses with specific prompts. Activation Through Cues: Memory and ChatGPT recall based on cues or prompts, influenced by input. Level of Detail: Memory constructs mental images based on information, just as ChatGPT responds to input details. Dynamic Nature: Both adapt to memorize repeated segments with diverse continuations. By understanding the dynamics of memory and its parallels with ChatGPT's response generation, researchers can further enhance the model's capabilities. Fine-tuning the model's ability to activate relevant information, generate specific responses, and adapt to varying levels of detail and specificity in the input can contribute to its overall performance and relevance in various language tasks.

Task repetition versus task rehearsal: Understanding effects of task-readiness factors and elemental genres on L2 writing task performance

In the literature on task-based language teaching (TBLT), task repetition and task rehearsal have frequently been used interchangeably. However, this distinction, identified as potentially significant (Ellis, 2019), is noteworthy due to the potential impact of participants’ awareness of future repetitions on second language (L2) task performance. Given the lack of empirical exploration regarding task repetition and task rehearsal distinction in the context of L2 writing, this study investigates how task rehearsal and task repetition affect the narrative and argumentative writing performances of 120 students of English as a second language (ESL) from a U.S. university. They were randomly assigned to four groups. Groups 1 and 3 wrote the narrative text initially, repeated the same task, and then proceeded to write the argumentative text, which was also repeated. The intervals between these performances were one week apart. However, Group 3 knew in advance that they were going to repeat the task, whereas Group 1 lacked this knowledge. Conversely, Groups 2 and 4 began by writing the argumentative text, repeated that task, and then proceeded to complete the narrative text, which was also repeated within the same time intervals. Group 4 had forewarning of their future performance, while Group 2 did not. We analysed the essays using the complexity, accuracy, lexis, and fluency (CALF) measures. The results indicated that time significantly influenced almost all CALF measures in narrative and argumentative writing, irrespective of the forewarning condition. Additionally, task readiness and essay order had a significant impact on most CALF measures, particularly in narrative writing. Specifically, task readiness positively affected the majority of CALF measures in narratives, whereas essay order exhibited sporadic influence.

An efficient multi‐scale transformer for satellite image dehazing

AbstractGiven the impressive achievement of convolutional neural networks (CNNs) in grasping image priors from extensive datasets, they have been widely utilized for tasks related to image restoration. Recently, there is been significant progress in another category of neural architectures—Transformers. These models have demonstrated remarkable performance in natural language tasks and higher‐level vision applications. Despite their ability to address some of CNNs limitations, such as restricted receptive fields and adaptability issues, Transformer models often face difficulties when processing images with a high level of detail. This is because the complexity of the computations required increases significantly with the image's spatial resolution. As a result, their application to most high‐resolution image restoration tasks becomes impractical. In our research, we introduce a novel Transformer model, named DehFormer, by implementing specific design modifications in its fundamental components, for example, the multi‐head attention and feed‐forward network. Specifically, the proposed architecture consists of the three modules, that is, (a) multi‐scale feature aggregation network (MSFAN), (b) the gated‐Dconv feed‐forward network (GFFN), (c) and the multi‐Dconv head transposed attention (MDHTA). For the MDHTA module, our objective is to scrutinize the mechanics of scaled dot‐product attention through the utilization of per‐element product operations, thereby bypassing the need for matrix multiplications and operating directly in the frequency domain for enhanced efficiency. For the GFFN module, which enables only the relevant and valuable information to advance through the network hierarchy, thereby enhancing the efficiency of information flow within the model. Extensive experiments are conducted on the SateHazelk, RS‐Haze, and RSID datasets, resulting in performance that significantly exceeds that of existing methods.

Semantic Similarity Analysis via Syntax Dependency Structure and Gate Recurrent Unit

Sentences are composed of words, phrases, and clauses. The relationship between them is usually tree-like. In the hierarchical structure of the sentence, the dependency relationships between different components affect the syntactic structure. Syntactic structure is very important for understanding the meaning of the whole sentence. However, the gated recursive unit (GRU) models cannot fully encode hierarchical syntactic dependencies, which leads to its poor performance in various natural language tasks. In this paper, a model called relative syntactic distance bidirectional gated recursive unit (RSD-BiGRU) is constructed to capture syntactic structure dependencies. The model modifies the gating mechanism in GRU through relative syntactic distance. It also offers a transformation gate to model the syntactic structure more directly. Embedding sentence meanings with sentence structure dependency into dense vectors. This model is used to conduct semantic similarity experiments on the QQP and SICK datasets. The results show that the sentence representation obtained by RSD-BiGRU model contains more semantic information. This is helpful for semantic similarity analysis tasks.

Latent Semantic and Disentangled Attention.

Sequential learning using transformer has achieved state-of-the-art performance in natural language tasks and many others. The key to this success is the multi-head self attention which encodes and gathers the features from individual tokens of an input sequence. The mapping or decoding is performed to produce an output sequence via cross attention. There are threefold weaknesses by using such an attention framework. First, since the attention would mix up the features of different tokens in input and output sequences, it is likely that redundant information exists in sequence data representation. Second, the patterns of attention weights among different heads tend to be similar. The model capacity is bounded. Third, the robustness in an encoder-decoder network against the model uncertainty is disregarded. To handle these weaknesses, this paper presents a Bayesian semantic and disentangled mask attention to learn latent disentanglement in multi-head attention where the redundant features in transformer are compensated with the latent topic information. The attention weights are filtered by a mask which is optimized through semantic clustering. This attention mechanism is implemented according to Bayesian learning for clustered disentanglement. The experiments on machine translation and speech recognition show the merit of Bayesian clustered disentanglement for mask attention.

Rumor detection methodology based on sentiment analysis and the transformer model with decision-level fusion

The use of transformer models in natural language processing (NLP) has gained significant attention in recent years due to their exceptional performance in various language tasks. This paper explores the application of transformer models in rumor detection, the relevant research on rumor detection, the use of transformer models, and the techniques used to boost the models performance. Ultimately, the purpose of this paper is to provide insight into the potential of transformer models in detecting rumors on social media. Unlike other rumor-detecting models, the author adds a sentiment analysis model as a supplement to rumor detection. Also, to address the issue of insufficient information in early-stage comments on rumors, this paper proposes a decision-level fusion method before the output layer, which effectively utilizes information from different sources and minimizes the negative impact of insufficient data sources. The early-stage rumor detection accuracy of the model is greatly enhanced by this method, therefore, the articles main contributions can be regarded as follows: First, this paper proposes a combination of an aspect level text sentiment analysis method according to syntactic features, gated recurrent units, and a self-attention mechanism. Experimental findings demonstrate that, compared to the original model without taking the sentiment analysis method into account, the proposed network model has advantages in accuracy and Macro F1 evaluation indexes. Second, a cross-text rumor-detecting method based on Decision-level fusion is proposed. Its advantage is that when the cross-text data source is incomplete and a certain text is missing, another text can be used to continue the analysis. Experimental findings show the effectiveness of this method in improving the accuracy of emotion recognition by integrating data from different modes. Third, a comparison is conducted between the performance of the Transformer-sentiment model and other related models, Text-CNN, Bi-LSTM, etc. The result shows that this integrated Transformer-sentiment model can not only solve the rumor detection tasks at higher accuracy, but can also overcome the shortcomings of the lack of datasets, which means that the model is more robust, and is able to detect rumors at the early stage of the rumor spreading process.

Blockwise compression of transformer-based models without retraining

Transformer-based models, exemplified by GPT-3, ChatGPT, and GPT-4, have recently garnered considerable attention in both academia and industry due to their promising performance in general language tasks. Nevertheless, these models typically involve computationally encoding processes, and in some cases, decoding processes as well, both of which are fundamentally large-scale matrix multiplication. These operations bring the inevitable challenges of massive computation resources and huge memory footprint, usually requiring at least 1023 FLOPs and hundreds of gigabytes, respectively. A common method to address this issue is to reduce the computational and memory requirements by applying layerwise quantization to the transformer, replacing the usual fp32 data type with a low-bit equivalent. Unfortunately, this method often leads to decreased model accuracy and necessitates time-consuming retraining. Such retraining not only requires fine-tuning skills but also substantial computational resources, posing challenges for users. To specifically tackle these issues, we propose BCT, a framework of blockwise compression for transformers without retraining, aiming to facilitate model deployment. Unlike layerwise compression methods, BCT achieves finer compression of the entire transformer by operating blockwise. This method mitigates data distribution deviation caused by quantization, eliminating the requirement for retraining. BCT effectively compresses all components of the model, including but not limited to the embedding, matrix multiplication, GELU, Softmax, layer normalization, and intermediate results. In a case study, an efficient model is compressed by BCT achieving up to 7.988x compression. Subsequently, we also evaluate it on several General Language Understanding Evaluation (GLUE) datasets. Experimental results on the majority of GLUE benchmark demonstrate the effectiveness of our method, as BCT achieves less than a 0.9% degradation in accuracy compared to the more than a 1% degradation seen with other methods providing similar or inferior compression ratios.

Performance analysis of large language models in the domain of legal argument mining.

Generative pre-trained transformers (GPT) have recently demonstrated excellent performance in various natural language tasks. The development of ChatGPT and the recently released GPT-4 model has shown competence in solving complex and higher-order reasoning tasks without further training or fine-tuning. However, the applicability and strength of these models in classifying legal texts in the context of argument mining are yet to be realized and have not been tested thoroughly. In this study, we investigate the effectiveness of GPT-like models, specifically GPT-3.5 and GPT-4, for argument mining via prompting. We closely study the model's performance considering diverse prompt formulation and example selection in the prompt via semantic search using state-of-the-art embedding models from OpenAI and sentence transformers. We primarily concentrate on the argument component classification task on the legal corpus from the European Court of Human Rights. To address these models' inherent non-deterministic nature and make our result statistically sound, we conducted 5-fold cross-validation on the test set. Our experiments demonstrate, quite surprisingly, that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for premise and conclusion classes, with 1.9% and 12% improvements, respectively. We hypothesize that the performance drop indirectly reflects the complexity of the structure in the dataset, which we verify through prompt and data analysis. Nevertheless, our results demonstrate a noteworthy variation in the performance of GPT models based on prompt formulation. We observe comparable performance between the two embedding models, with a slight improvement in the local model's ability for prompt selection. This suggests that local models are as semantically rich as the embeddings from the OpenAI model. Our results indicate that the structure of prompts significantly impacts the performance of GPT models and should be considered when designing them.

Glioblastoma remodelling of human neural circuits decreases survival

Gliomas synaptically integrate into neural circuits1,2. Previous research has demonstrated bidirectional interactions between neurons and glioma cells, with neuronal activity driving glioma growth1–4 and gliomas increasing neuronal excitability2,5–8. Here we sought to determine how glioma-induced neuronal changes influence neural circuits underlying cognition and whether these interactions influence patient survival. Using intracranial brain recordings during lexical retrieval language tasks in awake humans together with site-specific tumour tissue biopsies and cell biology experiments, we find that gliomas remodel functional neural circuitry such that task-relevant neural responses activate tumour-infiltrated cortex well beyond the cortical regions that are normally recruited in the healthy brain. Site-directed biopsies from regions within the tumour that exhibit high functional connectivity between the tumour and the rest of the brain are enriched for a glioblastoma subpopulation that exhibits a distinct synaptogenic and neuronotrophic phenotype. Tumour cells from functionally connected regions secrete the synaptogenic factor thrombospondin-1, which contributes to the differential neuron–glioma interactions observed in functionally connected tumour regions compared with tumour regions with less functional connectivity. Pharmacological inhibition of thrombospondin-1 using the FDA-approved drug gabapentin decreases glioblastoma proliferation. The degree of functional connectivity between glioblastoma and the normal brain negatively affects both patient survival and performance in language tasks. These data demonstrate that high-grade gliomas functionally remodel neural circuits in the human brain, which both promotes tumour progression and impairs cognition.

The use of anthroponyms in the «Foundations of the Academic Style of Speech» course when teaching Russian as a foreign language to philology students

The article is devoted to the study by foreign students of the philological profile of the „Fundamentals of the academic style of speech” course which is difficult to imagine without the use of anthroponyms. At the same time, anthroponyms act both as an object of study and as didactic teaching material. The author shows how, when mastering the academic style of speech, one can make the most of the names of philologists. The assimilation of personal names found in texts in the language of the specialty, the performance of language and speech tasks aimed at enhancing the use of anthroponymic units in the field of academic communication, contributes to the faster entry of foreign philology students into the academic language environment, improving their professional competence. The course „Fundamentals of the academic style of speech” for foreign students of philology is not only a means of communication, but also a tool for the formation of professional competence that helps them gain knowledge in the chosen profile and acquire the desired specialty

The Influence of Prior Linguistic Knowledge on Second Language Semantic Implicit Learning: Evidence from Cantonese–English Bilinguals

AbstractPrevious studies have shown that prior linguistic knowledge affects semantic implicit learning when stimuli are presented in the first language. We report an experiment that investigated whether such crosslinguistic influence from the first language would still emerge in the second language for semantic implicit learning of novel articles and fire/water semantic category mappings, a semantic distinction that is explicitly marked by semantic radicals in Chinese but not in English and a type of form–meaning connections that has not been investigated. We found that 30 Cantonese–English bilinguals and 30 native English speakers learned the target form–meaning connections and that the knowledge that they developed may have been implicit, as shown through post‐experiment verbal reports. Moreover, the bilingual group was significantly faster than the English group in distinguishing fire/water English nouns. These findings extend the range of semantic‐based regularities that can be learned at the implicit level and suggest that the markings of fire/water distinctions in Chinese affect second language task performance.

Comprehensive qualitative characterization of linguistic performance profiles in primary progressive aphasia: a multivariate study with FDG-PET

Primary progressive aphasia (PPA) classification relies on profile characterization of quantitatively impaired/spared performance in language tasks. In this study, we coextracted 8 qualitative types of errors in 67 PPA patients submitted to a comprehensive language assessment. Canonical correlation analysis was applied to simultaneously correlate qualitative errors and brain metabolism, collected with FDG-PET. Results showed the contribution of semantic, syntactic and working memory errors associated with specific correlates of regional metabolic changes. Reduced metabolism in the left fusiform gyrus, anterior-middle and inferior-temporal gyri and middle-temporal pole correlated with an increase of semantic errors. Hypometabolism in the left inferior, middle, and superior frontal gyri, insula and right middle-occipital gyrus was related to syntactic errors. Higher metabolism in the bilateral pallidum, putamen, and left thalamus, as well as hypometabolism in the left angular and supramarginal gyri, inferior-parietal lobule, posterior-middle and inferior-temporal gyri and posterior cingulum predicted the increase of working memory errors. A relevant role of working memory subcomponents was associated with distinct neural systems. Patients’ profiles are easily represented in a qualitative multidimensional space, in which mixed PPA overlapped with different phenotypes.

P01.13.A The effect of musicality on language recovery after awake glioma surgery

Abstract Background Awake craniotomy is used to resect tumor while preserving language. However, differences between patients in post-operative speech/language outcome are observed despite careful intra-operative monitoring. Literature describes improved performance in language tasks during cognitive tests in musicians. Moreover increased white matter connectivity properties in the corpus callosum are described in musicians compared to non-musicians. We hypothesize better recovery of language in musical patients after awake glioma surgery, caused by higher connectivity properties from the corpus callosum. Material and Methods Adult patients undergoing resection for glioma with an awake resection procedure at two neurosurgical centers were prospectively included. Patients without standardized language tests at pre- and post-operative level, with a glioblastoma multiforme (WHO grade 4) or undergoing re-resection were excluded. Language was assessed with the Diagnostic Instrument for Mild Aphasia (DIMA) and corrected for age and education years from a healthy population. The patients’ musical skill was assessed through questionnaires, and divided in groups based on the Musical Expertise Criterion (MEC) which defines musicality based the duration and intensity of musical training. Volumetric measures of the corpus callosum, corrected for total brain volumes, was calculated of each included patient based on the pre-operative structural MRI. Results Forty-six patients, enrolled between June 2015 and September 2019, were followed-up (mean/SD; 240/174 days after craniotomy) and divided in: non-musician (41.3%, n = 19), amateur-musician (34.8%, n=16) and trained-musician (23.9%, n = 11). Overall a decrease in language was observed after craniotomy (mean/SD) of -0.361/0.771. Musical abilities correlated with less decrease in language (mean/SD) when comparing non-musicians (-0.543/0.683) to amateur (-0.272/0.910) and trained (-0.176/0.693) musicians. An increased but non-significant trend (p=0.28) between musicality and corpus callosum / brain ratio (mean/SD) was observed in non-musicians (0.763, 0.718;0.808), amateur musicians (0.792, 0.745;0.838) and trained musicians (0.835, 0.778;0.891). Conclusion Musicality seemed to improve language outcome after awake glioma surgery, possibly attributed due to a higher white matter connectivity in the corpus callosum. Future studies with larger sample sizes are needed to confirm our findings.

Quantum computations for disambiguation and question answering

Automatic text processing is now a mature discipline in computer science, and so attempts at advancements using quantum computation have emerged as the new frontier, often under the term of quantum natural language processing. The main challenges consist in finding the most adequate ways of encoding words and their interactions on a quantum computer, considering hardware constraints, as well as building algorithms that take advantage of quantum architectures, so as to show improvement on the performance of natural language tasks. In this paper, we introduce a new framework that starts from a grammar that can be interpreted by means of tensor contraction, to build word representations as quantum states that serve as input to a quantum algorithm. We start by introducing an operator measurement to contract the representations of words, resulting in the representation of larger fragments of text. We then go on to develop pipelines for the tasks of sentence meaning disambiguation and question answering that take advantage of quantum features. For the first task, we show that our contraction scheme deals with syntactically ambiguous phrases storing the various different meanings in quantum superposition, a solution not available on a classical setting. For the second task, we obtain a question representation that contains all possible answers in equal quantum superposition, and we implement Grover’s quantum search algorithm to find the correct answer, agnostic to the specific question, an implementation with the potential of delivering a result with quadratic speedup.

Linguagem oral, processamento fonológico e memória visuoespacial em crianças com histórico de subnutrição leve na primeira infância

ABSTRACT Purpose To investigate the performance of children with a history of mild malnutrition in early childhood in tasks that assess oral language, phonological processing and visuospatial memory. Methods Thirty-six children participated (mean age 5.3 years; standard deviation = 0.57), with 9 children diagnosed with malnutrition between 0 and 3 years old (G1); 9 eutrophic and with language delay (G2) and; 18 eutrophic children with typical language development (G3). All were submitted to hearing screening procedures, diagnosis of current nutritional status and tests of language, phonological processing and working memory - visuospatial sketch. Statistical analysis was performed using the Test of Equality of Proportions and the Kruskal-Wallis test (α = 5%). Results Differences were observed in the performance in receptive and expressive language tasks of G1 and G2 in relation to G3. Performance on working memory tasks - visuospatial sketch was significantly different between G1 and G3 and G2 and G3. There was no difference between the groups concerning expressivevocabulary, phonological aspect, phonological awareness and phonological working memory. Conclusion Children with a history of mild malnutrition during the critical period of brain development may have language impairments, especially in the receptive area, and restricted performance in other cognitive skills, such as working memory - visuospatial sketch.

Pilot Study of Mindfulness Training on the Self-Awareness of Motor Symptoms in Parkinson's Disease - A Randomized Controlled Trial.

Objective: This study aims to evaluate feasibility and effects of a newly developed mindfulness intervention tailored to specific needs of patients with Parkinson’s disease (PD).Background: The phenomenon of impaired self-awareness of motor symptoms (ISAm) in PD might be reduced by increasing patients’ mindfulness. A PD-specific mindfulness intervention has been developed and evaluated as a potential treatment option: IPSUM (“Insight into Parkinson’s Disease Symptoms by using Mindfulness”).Methods: IPSUM’s effectiveness is evaluated by comparing an intervention with a waitlist-control group. Applying a pre-post design, patients were assessed before, directly after and 8weeks after treatment. The primary outcome was the change in a quantitative ISAm score from baseline to post-assessment. Secondary outcome measures were PD-related affective changes and neuropsychological test performance. Feasibility was evaluated via feedback forms.Results: In total, 30 non-depressed and non-demented PD patients were included (intervention: n=14, waitlist-control: n=16). ISAm score did not change significantly, but the training group showed greater performance in sustained attention and language tasks over time. Additional changes included greater mindfulness as well as less sleeping problems and anxiety. Cognitive disturbances, apathy, and sleeping problems worsened only in the waitlist-control group. Patients’ feedback regarding the training concept and material was excellent.Conclusion: Insight into Parkinson’s Disease Symptoms by using Mindfulness has not been capable of reducing ISAm in PD patients but appears to be a feasible and effective concept to, among others, support mental health in the mid-term. It has to be noted though that the study was stopped beforehand because of the SARS CoV-2 pandemic. The lack of findings might therefore be caused by a lack of statistical power. The need for further research to better understand the mechanisms of ISAm and its connection to mindfulness in PD is highlighted.

The impact of task description linguistic style on task performance: a text mining of crowdsourcing contests

PurposeExisting studies on the relationship between task description and task performance are insufficient, with many studies considering description length rather than content to measure quality or only evaluating a single aspect of task performance. To address this gap, this study analyzes the linguistic styles of task descriptions from 2,545 tasks on the Taskcn.com crowdsourcing platform.Design/methodology/approachAn empirical analysis was completed for task description language styles and task performance. The paper used text mining tool Simplified Chinese Linguistic Inquiry and Word Count to extract eight linguistic styles, namely readability, self-distancing, cognitive complexity, causality, tentative language, humanizing personal details, normative information and language intensity. And it tests the relationship between the eight language styles and task performance.FindingsThe study found that more cognitive complexity markers, tentative language, humanized details and normative information increase the quantity of submissions for a task. In addition, more humanized details and normative information in a task description improves the quality of task. Conversely, the inclusion of more causal relationships in a task description reduces the quantity of submissions. Poorer readability of the task description, less self-estrangement and higher language intensity reduces the quality of the task.Originality/valueThis study first reveals the importance of the linguistic styles used in task descriptions and provides a reference for how to attract more task solvers and achieve higher quality task performance by improving task descriptions. The research also enriches existing knowledge on the impact of linguistic styles and the applications of text mining.

Engagement of Language and Domain General Networks during Word Monitoring in a Native and Unknown Language.

Functional neuroimaging studies have highlighted the roles of three networks in processing language, all of which are typically left-lateralized: a ventral stream involved in semantics, a dorsal stream involved in phonology and speech production, and a more dorsal “multiple demand” network involved in many effortful tasks. As lateralization in all networks may be affected by life factors such as age, literacy, education, and brain pathology, we sought to develop a task paradigm with which to investigate the engagement of these networks, including manipulations to selectively emphasize semantic and phonological processing within a single task performable by almost anyone regardless of literacy status. In young healthy participants, we administered an auditory word monitoring task, in which participants had to note the occurrence of a target word within a continuous story presented in either their native language, Portuguese, or the unknown language, Japanese. Native language task performance activated ventral stream language networks, left lateralized but bilateral in the anterior temporal lobe. Unfamiliar language performance, being more difficult, activated left hemisphere dorsal stream structures and the multiple demand network bilaterally, but predominantly in the right hemisphere. These findings suggest that increased demands on phonological processing to accomplish word monitoring in the absence of semantic support may result in the bilateral recruitment of networks involved in speech perception under more challenging conditions.

AI-Mediated Communication

AI-Mediated Communication (AI-MC) is interpersonal communication that involves an artificially intelligent system that can modify, augment, or even generate content to achieve communicative and relational goals. AI-MC is increasingly involved in human communication and has the potential to impact core aspects of human communication, such as language production, interpersonal perception and task performance. Through a between-subjects experimental design we examine how these processes are influenced when integrating AI-generated language in the form of suggested text responses (Google's smart replies) into a text-based referential communication task. Our study replicates and extends the impacts of a positivity bias in AI-generated language and introduces the adjacency pair framework into the study of AI-MC. We also find preliminary yet mixed evidence to suggest that AI-generated language has the potential to undermine some dimensions of interpersonal perception, such as social attraction. This study contributes important concepts for future work in AI-MC and offers findings with implications for the design of AI systems in human-to-human communication.

Differential Behavioral and Neural Effects of Regional Cerebellar tDCS

The human cerebellum contributes to both motor and non-motor processes. Within the cerebellum, different subregions support sensorimotor and broader cognitive functions, due to regional patterns in anatomical connectivity with the cerebral cortex and spinal and vestibular systems. We evaluated the effects of transcranial direct current stimulation (tDCS) targeting different cerebellar regions on language task performance and whole-brain functional activation patterns. Functional MRI data were acquired while 43 healthy young adults (15 males, 28 females; 23.3 ± 3.0 years) performed a sentence completion task before and after 20 min of 1.5 mA anodal tDCS. Participants received tDCS targeting either the anterior sensorimotor cerebellum (n = 11; 3 cm right of inion, over lobule V); the right posterolateral cerebellum (n = 18; 1 cm down and 4 cm right of inion, over lobule VII); or sham tDCS (n = 14). TDCS targeting the right posterolateral cerebellum improved task accuracy relative to the sham condition (p = 0.04) and increased activation in left frontal and temporal cortices relevant to task performance (post-tDCS > pre-tDCS; T 3.17, FDR p < 0.05 cluster correction). The regions of increased BOLD signal after right posterolateral cerebellar tDCS fell within the network showing functional connectivity with right cerebellar lobule VII, suggesting specific modulation of this network. In contrast, tDCS targeting the sensorimotor cerebellum did not impact task performance and increased BOLD signal only in one cluster extending into the precentral gyrus. These findings indicate that sensorimotor and cognitive functional cerebellar subregions differentially impact behavioral task performance and task-relevant activation patterns, further contributing to our understanding of the cerebellar modulation of motor and non-motor functions.