Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple l...Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple label matching.However,previous studies have largely modeled this task as syndrome pattern classification within a fixed label space,which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning,and is also insufficient to capture the openness,semantic variability,and cross-disease reusability of syndrome manifestation expression.This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought(PR-CoT)supervision into large language models(LLMs)could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer.Methods Syndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information(X)→pathogenesis structure(Z)→syndrome pattern output(Y),where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment.Within this framework,a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders.After preprocessing,information extraction,manual proofreading,and data cleaning,the dataset comprised 4800 training cases,400 development cases,and 400 test cases.Each sample was annotated with a structured PR-CoT consisting of three progressive levels:clinical information summarization,comprehensive pathogenesis analysis,and syndrome pattern output.Supervised fine-tuning was conducted on open-source LLMs,with an end-to-end model serving as the baseline.Qwen3-32B was used as the primary experimental model,and Qwen3-14B as the scale comparison model.A progressive multidimensional evaluation framework was further established,comprising a structural parsing level,a semantic similarity level,and an expert blind review level.At the structural parsing level,syndrome pattern expressions were decomposed into structural elements and evaluated using Precision,Recall,F1 score,and Jaccard similarity.At the semantic similarity level,independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns.At the expert blind review level,three TCM experts independently evaluated model outputs on two dimensions:syndrome differentiation consistency and terminology standardization of syndrome patterns.In addition,zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets.Results At the structural parsing level,PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components.Compared with the corresponding baselines,neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision.In contrast,at the semantic similarity level,PR-CoT supervision produced stable positive gains across different model scales and evaluation systems.The average semantic score of Qwen3-32B increased from 6.4258 in the baseline model to 6.5850 after PR-CoT supervision,and that of Qwen3-14B increased from 5.8700 to 5.9642.At the expert blind review level,the overall score of Qwen3-32B(PR-CoT)was 7.0260±0.1077,higher than 6.4163±0.2889 for its baseline.In zero-shot cross-disease testing,the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets,indicating a certain degree of transferability.Conclusion The benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility,rather than in improved hard matching of structural elements.These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures,rather than as a classification task within a traditional fixed label space.By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework,this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment,interpretability,and multi-level evaluation.展开更多
Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to t...Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to tasks involving medical images.We provide an experimental insight into this phenomenon:CLIP is insensitive to the class names of medical images.For instance,replacing the class name“medulloblastoma”(a type of brain tumor)with“dog”in prompts has minimal impact on performance,a phenomenon not observed with natural images.To realign prompt learning with medical image recognition,we propose a novel prompt learning strategy,termed prompt reverse learning(PeLen).Different from the existing methods that adapt CLIP’s representations to downstream tasks,PeLen adapts task-specific representations to CLIP’s representations.Built upon the insensitivity to the class names of medical images,PeLen designates natural images and their class names to represent a specific class of medical images and class names,e.g.,allowing the image and text of a dog to correspond to the image and text of medulloblastoma.Consequently,PeLen learns prompts to align the representations between the medical images and the visual and textual representations of natural images.Our experiments demonstrate the efficacy of PeLen for medical image recognition.展开更多
War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient an...War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.展开更多
Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the con...Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.展开更多
The automatic diagnosis of depression plays a crucial role in preventing the deterioration of depression symptoms.The interview-based method is the most wildly adopted technique in depression diagnosis.However,the siz...The automatic diagnosis of depression plays a crucial role in preventing the deterioration of depression symptoms.The interview-based method is the most wildly adopted technique in depression diagnosis.However,the size of the collected conversation data is limited,and the sample distributions from different participants usually differ drastically.These factors present a great challenge in building a decent deep learning model for automatic depression diagnosis.Recently,large language models have demonstrated impressive capabilities and achieved human-level performance in various tasks under zero-shot and few-shot scenarios.This sheds new light on the development of AI solutions for domainspecific tasks with limited data.In this paper,we propose a two-stage approach that exploits the current most capable and cost-effective language model,ChatGPT,to make a depression diagnosis on interview-based data.Specifically,in the first stage,we use ChatGPT to summarize the raw dialogue sample,thereby facilitating the extraction of depression-related information.In the second stage,we use ChatGPT to classify the summarised data to predict the depressed state of the sample.Our method can achieve approximately 76%accuracy with a text-only modality on the DAIC-WOZ dataset.In addition,our method outperforms the performance of the state-of-the-art model by 6.2%in the D4 dataset.Our work highlights the potential of using large language models for diagnosis-based depression diagnosis.展开更多
More focuses of foreign language teaching theory and practice are on the study of first language and second language acquisition. The article deals with the concept of first and second language acquisition, the relati...More focuses of foreign language teaching theory and practice are on the study of first language and second language acquisition. The article deals with the concept of first and second language acquisition, the relationship between them and the factors influencing on them. The characteristics of the children acquisition and adult acquisition are also studied.展开更多
A new concept of language field and its value structure are presented in this paper for the first time and a describing framework is set forth for researching computational model of reasoning. On this basis we have es...A new concept of language field and its value structure are presented in this paper for the first time and a describing framework is set forth for researching computational model of reasoning. On this basis we have established a model of qualitative reasoning of causal relations and fuzzy integrated algorithm. Furthermore we have found a lot of its applications.展开更多
Sexism in the English Language is the reflector of a social sexism. The thesis is not only to discuss phenomena of sexism in English in terms of morphology, syntax and semantics, but also to focus on the origins and p...Sexism in the English Language is the reflector of a social sexism. The thesis is not only to discuss phenomena of sexism in English in terms of morphology, syntax and semantics, but also to focus on the origins and point out the ways of desexism.展开更多
Language variation is conditioned by complicated social factors such as class, age, occupation, education, sex, etc. all of which exert influences on one’s speech. Sex can be treated from two different angles in Euro...Language variation is conditioned by complicated social factors such as class, age, occupation, education, sex, etc. all of which exert influences on one’s speech. Sex can be treated from two different angles in European languages. One is biological sex; the other is gender (social gender). The social gender of a person plays a major role in influencing one’s speech.展开更多
Age is one of the factors which influnce foreign language learning, but not the most important one. Comparision on the effect of foreign language learning between adults and children cannot rely merely on age. So the ...Age is one of the factors which influnce foreign language learning, but not the most important one. Comparision on the effect of foreign language learning between adults and children cannot rely merely on age. So the question of an optimum age for foreign language learning is not a simple one which is only related to age. There are different optimum ages for different aims and demands of learning foreign language.展开更多
Sexism not only exists in the society, but is also reflected in English language. Although language can’t determine thought, in some sense, it does influence people’s views. In order to remind people of the harsh fa...Sexism not only exists in the society, but is also reflected in English language. Although language can’t determine thought, in some sense, it does influence people’s views. In order to remind people of the harsh fact and rectify the phenomenon, the author, by illustration and contrast, lists the manifestations of sexism in English, analyzes the causes and appeals to all the people for their contribution to the solution.展开更多
This paper empathizes the crucial importance of culture in ELT and analyzes the crosscultural communication block caused by differences between Western culture and Chinese culture on four aspects, which are differen...This paper empathizes the crucial importance of culture in ELT and analyzes the crosscultural communication block caused by differences between Western culture and Chinese culture on four aspects, which are different perception of the world, different perception of the self, different intercultural communication applications and different ideas of social relations. The author also puts forward some suggestions on how to solve the problem of crosscultural communication in English teaching,展开更多
Mistakes occur here and there when Chinese learners of English apply the foreign language in practice. This paper analyzes some common mistakes derived from cultural differences and tries to illustrate that language, ...Mistakes occur here and there when Chinese learners of English apply the foreign language in practice. This paper analyzes some common mistakes derived from cultural differences and tries to illustrate that language, as a part of culture should be taught and learnt as an integral part of learning about the target culture so that learners can gain a full understanding and good command of the foreign language.展开更多
During recent years,some notions about tasks have been considered as the major part of analysis in different teaching approaches and teachers are being more interested in the use of taskbased approach both in foreign ...During recent years,some notions about tasks have been considered as the major part of analysis in different teaching approaches and teachers are being more interested in the use of taskbased approach both in foreign and in second language teaching.The main goal of this article is to introduce and discuss some major principles of task-based language teaching and indicates how teachers can apply them in their curriculum.展开更多
It is self-evident that vocabulary work is the most essential and necessary task in foreign language accepition. English language teachers should develop students’ vocabulary learning skills.
Category-based statistic language model is an important method to solve the problem of sparse data.But there are two bottlenecks:1) The problem of word clustering.It is hard to find a suitable clustering method with g...Category-based statistic language model is an important method to solve the problem of sparse data.But there are two bottlenecks:1) The problem of word clustering.It is hard to find a suitable clustering method with good performance and less computation.2) Class-based method always loses the prediction ability to adapt the text in different domains.In order to solve above problems,a definition of word similarity by utilizing mutual information was presented.Based on word similarity,the definition of word set similarity was given.Experiments show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance,and the perplexity is reduced from 283 to 218.At the same time,an absolute weighted difference method was presented and was used to construct vari-gram language model which has good prediction ability.The perplexity of vari-gram model is reduced from 234.65 to 219.14 on Chinese corpora,and is reduced from 195.56 to 184.25 on English corpora compared with category-based model.展开更多
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
文摘Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple label matching.However,previous studies have largely modeled this task as syndrome pattern classification within a fixed label space,which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning,and is also insufficient to capture the openness,semantic variability,and cross-disease reusability of syndrome manifestation expression.This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought(PR-CoT)supervision into large language models(LLMs)could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer.Methods Syndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information(X)→pathogenesis structure(Z)→syndrome pattern output(Y),where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment.Within this framework,a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders.After preprocessing,information extraction,manual proofreading,and data cleaning,the dataset comprised 4800 training cases,400 development cases,and 400 test cases.Each sample was annotated with a structured PR-CoT consisting of three progressive levels:clinical information summarization,comprehensive pathogenesis analysis,and syndrome pattern output.Supervised fine-tuning was conducted on open-source LLMs,with an end-to-end model serving as the baseline.Qwen3-32B was used as the primary experimental model,and Qwen3-14B as the scale comparison model.A progressive multidimensional evaluation framework was further established,comprising a structural parsing level,a semantic similarity level,and an expert blind review level.At the structural parsing level,syndrome pattern expressions were decomposed into structural elements and evaluated using Precision,Recall,F1 score,and Jaccard similarity.At the semantic similarity level,independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns.At the expert blind review level,three TCM experts independently evaluated model outputs on two dimensions:syndrome differentiation consistency and terminology standardization of syndrome patterns.In addition,zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets.Results At the structural parsing level,PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components.Compared with the corresponding baselines,neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision.In contrast,at the semantic similarity level,PR-CoT supervision produced stable positive gains across different model scales and evaluation systems.The average semantic score of Qwen3-32B increased from 6.4258 in the baseline model to 6.5850 after PR-CoT supervision,and that of Qwen3-14B increased from 5.8700 to 5.9642.At the expert blind review level,the overall score of Qwen3-32B(PR-CoT)was 7.0260±0.1077,higher than 6.4163±0.2889 for its baseline.In zero-shot cross-disease testing,the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets,indicating a certain degree of transferability.Conclusion The benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility,rather than in improved hard matching of structural elements.These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures,rather than as a classification task within a traditional fixed label space.By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework,this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment,interpretability,and multi-level evaluation.
基金supported by the National Natural Science Foundation of China(62222117).
文摘Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to tasks involving medical images.We provide an experimental insight into this phenomenon:CLIP is insensitive to the class names of medical images.For instance,replacing the class name“medulloblastoma”(a type of brain tumor)with“dog”in prompts has minimal impact on performance,a phenomenon not observed with natural images.To realign prompt learning with medical image recognition,we propose a novel prompt learning strategy,termed prompt reverse learning(PeLen).Different from the existing methods that adapt CLIP’s representations to downstream tasks,PeLen adapts task-specific representations to CLIP’s representations.Built upon the insensitivity to the class names of medical images,PeLen designates natural images and their class names to represent a specific class of medical images and class names,e.g.,allowing the image and text of a dog to correspond to the image and text of medulloblastoma.Consequently,PeLen learns prompts to align the representations between the medical images and the visual and textual representations of natural images.Our experiments demonstrate the efficacy of PeLen for medical image recognition.
文摘War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.
基金supported by the National Natural Science Foundation of China(62222212).
文摘Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.
基金supported by the Science and Technology Innovation 2030 Project of China(2021ZD0202600).
文摘The automatic diagnosis of depression plays a crucial role in preventing the deterioration of depression symptoms.The interview-based method is the most wildly adopted technique in depression diagnosis.However,the size of the collected conversation data is limited,and the sample distributions from different participants usually differ drastically.These factors present a great challenge in building a decent deep learning model for automatic depression diagnosis.Recently,large language models have demonstrated impressive capabilities and achieved human-level performance in various tasks under zero-shot and few-shot scenarios.This sheds new light on the development of AI solutions for domainspecific tasks with limited data.In this paper,we propose a two-stage approach that exploits the current most capable and cost-effective language model,ChatGPT,to make a depression diagnosis on interview-based data.Specifically,in the first stage,we use ChatGPT to summarize the raw dialogue sample,thereby facilitating the extraction of depression-related information.In the second stage,we use ChatGPT to classify the summarised data to predict the depressed state of the sample.Our method can achieve approximately 76%accuracy with a text-only modality on the DAIC-WOZ dataset.In addition,our method outperforms the performance of the state-of-the-art model by 6.2%in the D4 dataset.Our work highlights the potential of using large language models for diagnosis-based depression diagnosis.
文摘More focuses of foreign language teaching theory and practice are on the study of first language and second language acquisition. The article deals with the concept of first and second language acquisition, the relationship between them and the factors influencing on them. The characteristics of the children acquisition and adult acquisition are also studied.
文摘A new concept of language field and its value structure are presented in this paper for the first time and a describing framework is set forth for researching computational model of reasoning. On this basis we have established a model of qualitative reasoning of causal relations and fuzzy integrated algorithm. Furthermore we have found a lot of its applications.
文摘Sexism in the English Language is the reflector of a social sexism. The thesis is not only to discuss phenomena of sexism in English in terms of morphology, syntax and semantics, but also to focus on the origins and point out the ways of desexism.
文摘Language variation is conditioned by complicated social factors such as class, age, occupation, education, sex, etc. all of which exert influences on one’s speech. Sex can be treated from two different angles in European languages. One is biological sex; the other is gender (social gender). The social gender of a person plays a major role in influencing one’s speech.
文摘Age is one of the factors which influnce foreign language learning, but not the most important one. Comparision on the effect of foreign language learning between adults and children cannot rely merely on age. So the question of an optimum age for foreign language learning is not a simple one which is only related to age. There are different optimum ages for different aims and demands of learning foreign language.
文摘Sexism not only exists in the society, but is also reflected in English language. Although language can’t determine thought, in some sense, it does influence people’s views. In order to remind people of the harsh fact and rectify the phenomenon, the author, by illustration and contrast, lists the manifestations of sexism in English, analyzes the causes and appeals to all the people for their contribution to the solution.
文摘This paper empathizes the crucial importance of culture in ELT and analyzes the crosscultural communication block caused by differences between Western culture and Chinese culture on four aspects, which are different perception of the world, different perception of the self, different intercultural communication applications and different ideas of social relations. The author also puts forward some suggestions on how to solve the problem of crosscultural communication in English teaching,
文摘Mistakes occur here and there when Chinese learners of English apply the foreign language in practice. This paper analyzes some common mistakes derived from cultural differences and tries to illustrate that language, as a part of culture should be taught and learnt as an integral part of learning about the target culture so that learners can gain a full understanding and good command of the foreign language.
文摘During recent years,some notions about tasks have been considered as the major part of analysis in different teaching approaches and teachers are being more interested in the use of taskbased approach both in foreign and in second language teaching.The main goal of this article is to introduce and discuss some major principles of task-based language teaching and indicates how teachers can apply them in their curriculum.
文摘It is self-evident that vocabulary work is the most essential and necessary task in foreign language accepition. English language teachers should develop students’ vocabulary learning skills.
基金Project(60763001) supported by the National Natural Science Foundation of ChinaProject(2010GZS0072) supported by the Natural Science Foundation of Jiangxi Province,ChinaProject(GJJ12271) supported by the Science and Technology Foundation of Provincial Education Department of Jiangxi Province,China
文摘Category-based statistic language model is an important method to solve the problem of sparse data.But there are two bottlenecks:1) The problem of word clustering.It is hard to find a suitable clustering method with good performance and less computation.2) Class-based method always loses the prediction ability to adapt the text in different domains.In order to solve above problems,a definition of word similarity by utilizing mutual information was presented.Based on word similarity,the definition of word set similarity was given.Experiments show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance,and the perplexity is reduced from 283 to 218.At the same time,an absolute weighted difference method was presented and was used to construct vari-gram language model which has good prediction ability.The perplexity of vari-gram model is reduced from 234.65 to 219.14 on Chinese corpora,and is reduced from 195.56 to 184.25 on English corpora compared with category-based model.
