{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/linear-warmup-with-linear-decay/papers/26","list_of":"/method/linear-warmup-with-linear-decay","method":"Linear Warmup With Linear Decay","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":26,"pages_in_order":71,"rows_per_page":100,"rows":[2501,2600],"of":7076,"counts":{"archive_papers_tagged":7076,"with_a_code_link":2913,"where_syntology_ran_a_sample":650,"not_listed_spam_title":0,"listed":7076,"listed_where_code_ran":650,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":531,"every_run_a_failure_of_syntologys_instrument":119,"listed_with_a_run_with_no_instrument_failure":531,"listed_every_run_a_failure_of_syntologys_instrument":119,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/linear-warmup-with-linear-decay","prev":"/method/linear-warmup-with-linear-decay/papers/25","next":"/method/linear-warmup-with-linear-decay/papers/27","papers":[{"paper":null,"slug":"leave-no-place-behind-improved-geolocation-in","title":"Leave no Place Behind: Improved Geolocation in Humanitarian Documents","date":"2023-09-06","arxiv_id":"2309.02914","n_code_links":0,"syntology":null},{"paper":"/paper/offensive-hebrew-corpus-and-detection-using","slug":"offensive-hebrew-corpus-and-detection-using","title":"Offensive Hebrew Corpus and Detection using BERT","date":"2023-09-06","arxiv_id":"2309.02724","n_code_links":1,"syntology":null},{"paper":null,"slug":"self-supervised-masked-digital-elevation","title":"Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks","date":"2023-09-06","arxiv_id":"2309.03367","n_code_links":0,"syntology":null},{"paper":null,"slug":"incorporating-dictionaries-into-a-neural","title":"Incorporating Dictionaries into a Neural Network Architecture to Extract COVID-19 Medical Concepts From Social Media","date":"2023-09-05","arxiv_id":"2309.02188","n_code_links":0,"syntology":null},{"paper":null,"slug":"language-models-for-novelty-detection-in","title":"Language Models for Novelty Detection in System Call Traces","date":"2023-09-05","arxiv_id":"2309.02206","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-bert-language-models-for-multi","title":"Leveraging BERT Language Models for Multi-Lingual ESG Issue Identification","date":"2023-09-05","arxiv_id":"2309.02189","n_code_links":0,"syntology":null},{"paper":null,"slug":"sample-size-in-natural-language-processing","title":"Sample Size in Natural Language Processing within Healthcare Research","date":"2023-09-05","arxiv_id":"2309.02237","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-large-language-models-in","slug":"benchmarking-large-language-models-in","title":"Benchmarking Large Language Models in Retrieval-Augmented Generation","date":"2023-09-04","arxiv_id":"2309.01431","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["chen700564/RGB"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-study-on-the-implementation-of-generative","title":"A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture","date":"2023-09-03","arxiv_id":"2309.01105","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-visual-interpretation-based-self-improved","title":"A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training","date":"2023-09-03","arxiv_id":"2309.01196","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-graph-embeddings-for-multi-lingual","title":"Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports","date":"2023-09-02","arxiv_id":"2309.00917","n_code_links":0,"syntology":null},{"paper":null,"slug":"studying-the-impacts-of-pre-training-using","title":"Studying the impacts of pre-training using ChatGPT-generated text on downstream tasks","date":"2023-09-02","arxiv_id":"2309.05668","n_code_links":0,"syntology":null},{"paper":"/paper/batchprompt-accomplish-more-with-less","slug":"batchprompt-accomplish-more-with-less","title":"BatchPrompt: Accomplish more with less","date":"2023-09-01","arxiv_id":"2309.00384","n_code_links":1,"syntology":null},{"paper":null,"slug":"sortednet-a-place-for-every-network-and-every","title":"SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks","date":"2023-09-01","arxiv_id":"2309.00255","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-humans-help-bert-gain-confidence","title":"Can humans help BERT gain \"confidence\"?","date":"2023-08-31","arxiv_id":"2309.06580","n_code_links":0,"syntology":null},{"paper":null,"slug":"dictabert-a-state-of-the-art-bert-suite-for","title":"DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew","date":"2023-08-31","arxiv_id":"2308.16687","n_code_links":0,"syntology":null},{"paper":null,"slug":"linking-microblogging-sentiments-to-stock","title":"Linking microblogging sentiments to stock price movement: An application of GPT-4","date":"2023-08-31","arxiv_id":"2308.16771","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-improving-the-expressiveness-of","title":"Towards Improving the Expressiveness of Singing Voice Synthesis with BERT Derived Semantic Information","date":"2023-08-31","arxiv_id":"2308.16836","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-compression-via-subspace","slug":"transformer-compression-via-subspace","title":"$\\rm SP^3$: Enhancing Structured Pruning via PCA Projection","date":"2023-08-31","arxiv_id":"2308.16475","n_code_links":1,"syntology":{"ran":12,"of":14,"n_ran_checked":11,"n_instrument":1,"unverified":2,"pointer_only":14,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["hyx1999/sp3"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"analyzing-character-and-consciousness-in-ai","title":"Analyzing Character and Consciousness in AI-Generated Social Content: A Case Study of Chirper, the AI Social Network","date":"2023-08-30","arxiv_id":"2309.08614","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-deepzen-speech-synthesis-system-for","title":"The DeepZen Speech Synthesis System for Blizzard Challenge 2023","date":"2023-08-30","arxiv_id":"2308.15945","n_code_links":0,"syntology":null},{"paper":"/paper/spikebert-a-language-spikformer-trained-with","slug":"spikebert-a-language-spikformer-trained-with","title":"SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation","date":"2023-08-29","arxiv_id":"2308.15122","n_code_links":1,"syntology":{"ran":8,"of":9,"n_ran_checked":8,"n_instrument":0,"unverified":1,"pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["Lvchangze/SpikeBERT"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/aner-arabic-and-arabizi-named-entity","slug":"aner-arabic-and-arabizi-named-entity","title":"ANER: Arabic and Arabizi Named Entity Recognition using Transformer-Based Approach","date":"2023-08-28","arxiv_id":"2308.14669","n_code_links":1,"syntology":null},{"paper":null,"slug":"target-independent-xla-optimization-using","title":"Target-independent XLA optimization using Reinforcement Learning","date":"2023-08-28","arxiv_id":"2308.14364","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-knowledge-distillation-for-bert","title":"Improving Knowledge Distillation for BERT Models: Loss Functions, Mapping Methods, and Weight Tuning","date":"2023-08-26","arxiv_id":"2308.13958","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-knowledge-and-reinforcement","title":"Leveraging Knowledge and Reinforcement Learning for Enhanced Reliability of Language Models","date":"2023-08-25","arxiv_id":"2308.13467","n_code_links":0,"syntology":null},{"paper":"/paper/a-small-and-fast-bert-for-chinese-medical","slug":"a-small-and-fast-bert-for-chinese-medical","title":"A Small and Fast BERT for Chinese Medical Punctuation Restoration","date":"2023-08-24","arxiv_id":"2308.12568","n_code_links":1,"syntology":null},{"paper":"/paper/advancing-hungarian-text-processing-with","slug":"advancing-hungarian-text-processing-with","title":"Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines","date":"2023-08-24","arxiv_id":"2308.12635","n_code_links":2,"syntology":null},{"paper":null,"slug":"multi-bert-for-embeddings-for-recommendation","title":"Multi-BERT for Embeddings for Recommendation System","date":"2023-08-24","arxiv_id":"2308.13050","n_code_links":0,"syntology":null},{"paper":"/paper/sentence-embedding-models-for-ancient-greek","slug":"sentence-embedding-models-for-ancient-greek","title":"Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation","date":"2023-08-24","arxiv_id":"2308.13116","n_code_links":2,"syntology":null},{"paper":null,"slug":"text-similarity-from-image-contents-using","title":"Text Similarity from Image Contents using Statistical and Semantic Analysis Techniques","date":"2023-08-24","arxiv_id":"2308.12842","n_code_links":0,"syntology":null},{"paper":null,"slug":"simple-is-better-and-large-is-not-enough","title":"Simple is Better and Large is Not Enough: Towards Ensembling of Foundational Language Models","date":"2023-08-23","arxiv_id":"2308.12272","n_code_links":0,"syntology":null},{"paper":"/paper/spikingbert-distilling-bert-to-train-spiking","slug":"spikingbert-distilling-bert-to-train-spiking","title":"SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation","date":"2023-08-21","arxiv_id":"2308.10873","n_code_links":1,"syntology":{"ran":13,"of":17,"n_ran_checked":8,"n_instrument":5,"unverified":4,"pointer_only":17,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","official":{"repos":["neurocomplab-psu/spikingbert"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/how-good-are-large-language-models-at-out-of","slug":"how-good-are-large-language-models-at-out-of","title":"How Good Are LLMs at Out-of-Distribution Detection?","date":"2023-08-20","arxiv_id":"2308.10261","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["awenbocc/llm-ood"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/improving-adversarial-robustness-of-masked","slug":"improving-adversarial-robustness-of-masked","title":"Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting","date":"2023-08-20","arxiv_id":"2308.10315","n_code_links":1,"syntology":null},{"paper":null,"slug":"east-efficient-and-accurate-secure","title":"East: Efficient and Accurate Secure Transformer Framework for Inference","date":"2023-08-19","arxiv_id":"2308.09923","n_code_links":0,"syntology":null},{"paper":null,"slug":"open-closed-or-small-language-models-for-text","title":"Open, Closed, or Small Language Models for Text Classification?","date":"2023-08-19","arxiv_id":"2308.10092","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-multi-class-text-classification-a","title":"Optimizing Multi-Class Text Classification: A Diverse Stacking Ensemble Framework Utilizing Transformers","date":"2023-08-19","arxiv_id":"2308.11519","n_code_links":0,"syntology":null},{"paper":"/paper/learning-representations-on-logs-for-aiops","slug":"learning-representations-on-logs-for-aiops","title":"Learning Representations on Logs for AIOps","date":"2023-08-18","arxiv_id":"2308.11526","n_code_links":1,"syntology":null},{"paper":"/paper/predictive-authoring-for-brazilian-portuguese","slug":"predictive-authoring-for-brazilian-portuguese","title":"Predictive Authoring for Brazilian Portuguese Augmentative and Alternative Communication","date":"2023-08-18","arxiv_id":"2308.09497","n_code_links":1,"syntology":null},{"paper":"/paper/a-comparative-study-of-text-embedding-models","slug":"a-comparative-study-of-text-embedding-models","title":"A Comparative Study of Text Embedding Models for Semantic Text Similarity in Bug Reports","date":"2023-08-17","arxiv_id":"2308.09193","n_code_links":1,"syntology":null},{"paper":"/paper/bioptimus-pre-training-an-optimal-biomedical","slug":"bioptimus-pre-training-an-optimal-biomedical","title":"BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognition","date":"2023-08-16","arxiv_id":"2308.08625","n_code_links":1,"syntology":null},{"paper":null,"slug":"beware-of-deception-detecting-half-truth-and","title":"\"Beware of deception\": Detecting Half-Truth and Debunking it through Controlled Claim Editing","date":"2023-08-15","arxiv_id":"2308.07973","n_code_links":0,"syntology":null},{"paper":null,"slug":"ds4dh-at-smm4h-2023-zero-shot-adverse-drug","title":"DS4DH at #SMM4H 2023: Zero-Shot Adverse Drug Events Normalization using Sentence Transformers and Reciprocal-Rank Fusion","date":"2023-08-15","arxiv_id":"2308.12877","n_code_links":0,"syntology":null},{"paper":null,"slug":"finding-stakeholder-material-information-from","title":"Finding Stakeholder-Material Information from 10-K Reports using Fine-Tuned BERT and LSTM Models","date":"2023-08-15","arxiv_id":"2308.07522","n_code_links":0,"syntology":null},{"paper":null,"slug":"multischubert-effective-multimodal-fusion-for","title":"MultiSChuBERT: Effective Multimodal Fusion for Scholarly Document Quality Prediction","date":"2023-08-15","arxiv_id":"2308.07971","n_code_links":0,"syntology":null},{"paper":null,"slug":"spm-structured-pretraining-and-matching","title":"SPM: Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search","date":"2023-08-15","arxiv_id":"2308.07711","n_code_links":0,"syntology":null},{"paper":"/paper/synthesizing-political-zero-shot-relation","slug":"synthesizing-political-zero-shot-relation","title":"Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification","date":"2023-08-15","arxiv_id":"2308.07876","n_code_links":1,"syntology":null},{"paper":"/paper/ternary-singular-value-decomposition-as-a","slug":"ternary-singular-value-decomposition-as-a","title":"Ternary Singular Value Decomposition as a Better Parameterized Form in Linear Mapping","date":"2023-08-15","arxiv_id":"2308.07641","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ozzzp/ternary_decompose"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"an-ensemble-approach-to-question","title":"An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM","date":"2023-08-13","arxiv_id":"2308.06828","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-face-recognition-from-caption","title":"Improving Face Recognition from Caption Supervision with Multi-Granular Contextual Feature Aggregation","date":"2023-08-13","arxiv_id":"2308.06866","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-phenotype-recognition-in-clinical","slug":"enhancing-phenotype-recognition-in-clinical","title":"Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT","date":"2023-08-11","arxiv_id":"2308.06294","n_code_links":1,"syntology":null},{"paper":"/paper/identification-of-the-relevance-of-comments","slug":"identification-of-the-relevance-of-comments","title":"Identification of the Relevance of Comments in Codes Using Bag of Words and Transformer Based Models","date":"2023-08-11","arxiv_id":"2308.06144","n_code_links":1,"syntology":null},{"paper":null,"slug":"task-conditioned-bert-for-joint-intent","title":"Task Conditioned BERT for Joint Intent Detection and Slot-filling","date":"2023-08-11","arxiv_id":"2308.06165","n_code_links":0,"syntology":null},{"paper":null,"slug":"bringing-order-into-the-realm-of-transformer","title":"Bringing order into the realm of Transformer-based language models for artificial intelligence and law","date":"2023-08-10","arxiv_id":"2308.05502","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-machine-learning-and-transformer","title":"Exploring Machine Learning and Transformer-based Approaches for Deceptive Text Classification: A Comparative Analysis","date":"2023-08-10","arxiv_id":"2308.05476","n_code_links":0,"syntology":null},{"paper":null,"slug":"metroberta-leveraging-traditional-customer","title":"MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model","date":"2023-08-09","arxiv_id":"2308.05012","n_code_links":0,"syntology":null},{"paper":"/paper/performance-analysis-of-transformer-based","slug":"performance-analysis-of-transformer-based","title":"Performance Analysis of Transformer Based Models (BERT, ALBERT and RoBERTa) in Fake News Detection","date":"2023-08-09","arxiv_id":"2308.04950","n_code_links":1,"syntology":null},{"paper":"/paper/a-cross-domain-evaluation-of-approaches-for","slug":"a-cross-domain-evaluation-of-approaches-for","title":"A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction","date":"2023-08-07","arxiv_id":"2308.03891","n_code_links":1,"syntology":null},{"paper":"/paper/analysis-of-the-evolution-of-advanced","slug":"analysis-of-the-evolution-of-advanced","title":"Analysis of the Evolution of Advanced Transformer-Based Language Models: Experiments on Opinion Mining","date":"2023-08-07","arxiv_id":"2308.03235","n_code_links":1,"syntology":null},{"paper":null,"slug":"detecting-spells-in-fantasy-literature-with-a","title":"Detecting Spells in Fantasy Literature with a Transformer Based Artificial Intelligence","date":"2023-08-07","arxiv_id":"2308.03660","n_code_links":0,"syntology":null},{"paper":null,"slug":"trusting-language-models-in-education","title":"Trusting Language Models in Education","date":"2023-08-07","arxiv_id":"2308.03866","n_code_links":0,"syntology":null},{"paper":null,"slug":"what-about-translation-new-coding-system-for","title":"Training BERT Models to Carry Over a Coding System Developed on One Corpus to Another","date":"2023-08-07","arxiv_id":"2308.03742","n_code_links":0,"syntology":null},{"paper":"/paper/end-to-end-query-term-weighting","slug":"end-to-end-query-term-weighting","title":"End-to-End Query Term Weighting","date":"2023-08-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/damstf-domain-adversarial-learning-enhanced","slug":"damstf-domain-adversarial-learning-enhanced","title":"DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation","date":"2023-08-05","arxiv_id":"2308.02753","n_code_links":0,"syntology":{"ran":3,"of":7,"n_ran_checked":3,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":null,"slug":"an-apt-event-extraction-method-based-on-bert","title":"An APT Event Extraction Method Based on BERT-BiGRU-CRF for APT Attack Detection","date":"2023-08-04","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/explaining-relation-classification-models","slug":"explaining-relation-classification-models","title":"Explaining Relation Classification Models with Semantic Extents","date":"2023-08-04","arxiv_id":"2308.02193","n_code_links":2,"syntology":null},{"paper":null,"slug":"meta-tsallis-entropy-minimization-a-new-self","title":"Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification","date":"2023-08-04","arxiv_id":"2308.02746","n_code_links":0,"syntology":null},{"paper":"/paper/baby-s-cothought-leveraging-large-language","slug":"baby-s-cothought-leveraging-large-language","title":"Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models","date":"2023-08-03","arxiv_id":"2308.01684","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparing-scalable-strategies-for-generating","title":"Comparing scalable strategies for generating numerical perspectives","date":"2023-08-03","arxiv_id":"2308.01535","n_code_links":0,"syntology":null},{"paper":null,"slug":"food-classification-using-joint","title":"Food Classification using Joint Representation of Visual and Textual Data","date":"2023-08-03","arxiv_id":"2308.02562","n_code_links":0,"syntology":null},{"paper":"/paper/local-large-language-models-for-complex","slug":"local-large-language-models-for-complex","title":"Local Large Language Models for Complex Structured Medical Tasks","date":"2023-08-03","arxiv_id":"2308.01727","n_code_links":1,"syntology":null},{"paper":null,"slug":"bio-clinical-bert-bert-base-and-cnn","title":"Bio+Clinical BERT, BERT Base, and CNN Performance Comparison for Predicting Drug-Review Satisfaction","date":"2023-08-02","arxiv_id":"2308.03782","n_code_links":0,"syntology":null},{"paper":"/paper/contextual-emotion-recognition-using","slug":"contextual-emotion-recognition-using","title":"Contextual Emotion Recognition Using Transformer-Based Models","date":"2023-08-02","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-better-query-classification-with","title":"Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search","date":"2023-08-02","arxiv_id":"2308.01098","n_code_links":0,"syntology":null},{"paper":"/paper/retrieval-augmented-generation-and","slug":"retrieval-augmented-generation-and","title":"Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education","date":"2023-08-01","arxiv_id":"2308.00479","n_code_links":2,"syntology":null},{"paper":"/paper/self-supervised-contrastive-bert-fine-tuning","slug":"self-supervised-contrastive-bert-fine-tuning","title":"Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval","date":"2023-08-01","arxiv_id":"2308.00762","n_code_links":2,"syntology":null},{"paper":"/paper/classifying-multilingual-party-manifestos","slug":"classifying-multilingual-party-manifestos","title":"Classifying multilingual party manifestos: Domain transfer across country, time, and genre","date":"2023-07-31","arxiv_id":"2307.16511","n_code_links":1,"syntology":null},{"paper":"/paper/noisy-self-training-with-data-augmentations","slug":"noisy-self-training-with-data-augmentations","title":"Noisy Self-Training with Data Augmentations for Offensive and Hate Speech Detection Tasks","date":"2023-07-31","arxiv_id":"2307.16609","n_code_links":1,"syntology":null},{"paper":"/paper/vacancysbert-the-approach-for-representation","slug":"vacancysbert-the-approach-for-representation","title":"VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domain","date":"2023-07-31","arxiv_id":"2307.16638","n_code_links":1,"syntology":null},{"paper":null,"slug":"laficmil-rethinking-large-file-classification","title":"LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning","date":"2023-07-30","arxiv_id":"2308.01413","n_code_links":0,"syntology":null},{"paper":null,"slug":"tutorials-on-stance-detection-using-pre","title":"Tutorials on Stance Detection using Pre-trained Language Models: Fine-tuning BERT and Prompting Large Language Models","date":"2023-07-28","arxiv_id":"2307.15331","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-generative-models-for-graph-to","slug":"evaluating-generative-models-for-graph-to","title":"Evaluating Generative Models for Graph-to-Text Generation","date":"2023-07-27","arxiv_id":"2307.14712","n_code_links":1,"syntology":null},{"paper":"/paper/new-interaction-paradigm-for-complex-eda","slug":"new-interaction-paradigm-for-complex-eda","title":"New Interaction Paradigm for Complex EDA Software Leveraging GPT","date":"2023-07-27","arxiv_id":"2307.14740","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["smarton-empower/smarton-ai"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"textmania-enriching-visual-feature-by-text","title":"TextManiA: Enriching Visual Feature by Text-driven Manifold Augmentation","date":"2023-07-27","arxiv_id":"2307.14611","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-libraries-for-the","title":"Comparative Analysis of Libraries for the Sentimental Analysis","date":"2023-07-26","arxiv_id":"2307.14311","n_code_links":0,"syntology":null},{"paper":null,"slug":"developing-and-evaluating-tiny-to-medium","title":"Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models","date":"2023-07-26","arxiv_id":"2307.14134","n_code_links":0,"syntology":null},{"paper":null,"slug":"dpbert-efficient-inference-for-bert-based-on","title":"DPBERT: Efficient Inference for BERT based on Dynamic Planning","date":"2023-07-26","arxiv_id":"2308.00108","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-hybrid-machine-learning-model-for","title":"A Hybrid Machine Learning Model for Classifying Gene Mutations in Cancer using LSTM, BiLSTM, CNN, GRU, and GloVe","date":"2023-07-24","arxiv_id":"2307.14361","n_code_links":0,"syntology":null},{"paper":"/paper/chatgpt-for-software-security-exploring-the","slug":"chatgpt-for-software-security-exploring-the","title":"How Does Naming Affect LLMs on Code Analysis Tasks?","date":"2023-07-24","arxiv_id":"2307.12488","n_code_links":0,"syntology":null},{"paper":"/paper/identifying-misinformation-on-youtube-through","slug":"identifying-misinformation-on-youtube-through","title":"Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models","date":"2023-07-22","arxiv_id":"2307.12155","n_code_links":1,"syntology":null},{"paper":null,"slug":"deftri-a-few-shot-label-fused-contextual-1","title":"DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce","date":"2023-07-21","arxiv_id":"2307.11344","n_code_links":0,"syntology":null},{"paper":"/paper/the-looming-threat-of-fake-and-llm-generated","slug":"the-looming-threat-of-fake-and-llm-generated","title":"The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention","date":"2023-07-21","arxiv_id":"2307.11864","n_code_links":1,"syntology":null},{"paper":"/paper/a-systematic-evaluation-of-federated-learning","slug":"a-systematic-evaluation-of-federated-learning","title":"An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing","date":"2023-07-20","arxiv_id":"2307.11254","n_code_links":2,"syntology":null},{"paper":"/paper/generative-language-models-on-nucleotide","slug":"generative-language-models-on-nucleotide","title":"Generative Language Models on Nucleotide Sequences of Human Genes","date":"2023-07-20","arxiv_id":"2307.10634","n_code_links":1,"syntology":null},{"paper":null,"slug":"mood-classification-of-bangla-songs-based-on","title":"Mood Classification of Bangla Songs Based on Lyrics","date":"2023-07-19","arxiv_id":"2307.10314","n_code_links":0,"syntology":null},{"paper":"/paper/sprint-a-unified-toolkit-for-evaluating-and","slug":"sprint-a-unified-toolkit-for-evaluating-and","title":"SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval","date":"2023-07-19","arxiv_id":"2307.10488","n_code_links":1,"syntology":null},{"paper":"/paper/analyzing-sports-commentary-in-order-to","slug":"analyzing-sports-commentary-in-order-to","title":"Analyzing sports commentary in order to automatically recognize events and extract insights","date":"2023-07-18","arxiv_id":"2307.10303","n_code_links":2,"syntology":null},{"paper":"/paper/application-of-bert-in-wind-power-forecasting","slug":"application-of-bert-in-wind-power-forecasting","title":"Application of BERT in Wind Power Forecasting-Teletraan's Solution in Baidu KDD Cup 2022","date":"2023-07-18","arxiv_id":"2307.09248","n_code_links":1,"syntology":null},{"paper":null,"slug":"automated-ableism-an-exploration-of-explicit","title":"Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models","date":"2023-07-18","arxiv_id":"2307.09209","n_code_links":0,"syntology":null}],"record_sha256":"58aff1f5302a19133db944179614152fe153189b18a91a83aa521ad796a375af","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}