{"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":"/task/language-modeling/papers/110","list_of":"/task/language-modeling","task":"Language Modeling","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":110,"pages_in_order":142,"rows_per_page":100,"rows":[10901,11000],"of":14182,"counts":{"archive_papers_tagged":14182,"with_a_code_link":5620,"where_syntology_ran_a_sample":1894,"not_listed_spam_title":0,"listed":14182,"listed_where_code_ran":1894,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1580,"every_run_a_failure_of_syntologys_instrument":314,"listed_with_a_run_with_no_instrument_failure":1580,"listed_every_run_a_failure_of_syntologys_instrument":314,"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":"/task/language-modeling","prev":"/task/language-modeling/papers/109","next":"/task/language-modeling/papers/111","papers":[{"url":null,"slug":"cross-modal-similarity-based-curriculum","title":"Cross-Modal Similarity-Based Curriculum Learning for Image Captioning","date":"2022-12-14","arxiv_id":"2212.07075","repositories_listed":0,"syntology":null},{"url":null,"slug":"manta-efficient-gradient-based-tokenization","title":"MANTa: Efficient Gradient-Based Tokenization for Robust End-to-End Language Modeling","date":"2022-12-14","arxiv_id":"2212.07284","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-image-style-transfer-from-freeform-text","title":"Deep Image Style Transfer from Freeform Text","date":"2022-12-13","arxiv_id":"2212.06868","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-competition-solution-for","title":"Technical Report -- Competition Solution for Prompt Tuning using Pretrained Language Model","date":"2022-12-13","arxiv_id":"2212.06369","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-challenges-of-htr-model-training-feedback","title":"The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique","date":"2022-12-13","arxiv_id":"2212.11146","repositories_listed":0,"syntology":null},{"url":null,"slug":"cno-lstm-a-chaotic-neural-oscillatory-long","title":"CNO-LSTM: A Chaotic Neural Oscillatory Long Short-Term Memory Model for Text Classification","date":"2022-12-12","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-knowledge-graph-service-for","title":"A Unified Knowledge Graph Augmentation Service for Boosting Domain-specific NLP Tasks","date":"2022-12-10","arxiv_id":"2212.05251","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-text-detection-with-multiple","title":"Artificial Text Detection with Multiple Training Strategies","date":"2022-12-10","arxiv_id":"2212.05194","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-information-extraction-from","title":"Structured information extraction from complex scientific text with fine-tuned large language models","date":"2022-12-10","arxiv_id":"2212.05238","repositories_listed":0,"syntology":null},{"url":null,"slug":"uniform-masking-prevails-in-vision-language","title":"Uniform Masking Prevails in Vision-Language Pretraining","date":"2022-12-10","arxiv_id":"2212.05195","repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-causality-in-gpt-2-a-case-study","title":"Implicit causality in GPT-2: a case study","date":"2022-12-08","arxiv_id":"2212.04348","repositories_listed":0,"syntology":null},{"url":null,"slug":"pivotal-role-of-language-modeling-in","title":"Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning","date":"2022-12-07","arxiv_id":"2212.03760","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-with-scientific-text-improves","title":"Pre-Training With Scientific Text Improves Educational Question Generation","date":"2022-12-07","arxiv_id":"2212.03869","repositories_listed":0,"syntology":null},{"url":null,"slug":"adir-adaptive-diffusion-for-image","title":"ADIR: Adaptive Diffusion for Image Reconstruction","date":"2022-12-06","arxiv_id":"2212.03221","repositories_listed":0,"syntology":null},{"url":null,"slug":"cysecbert-a-domain-adapted-language-model-for","title":"CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain","date":"2022-12-06","arxiv_id":"2212.02974","repositories_listed":0,"syntology":null},{"url":null,"slug":"m-vader-a-model-for-diffusion-with-multimodal","title":"M-VADER: A Model for Diffusion with Multimodal Context","date":"2022-12-06","arxiv_id":"2212.02936","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-audio-visual-speech","title":"Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation","date":"2022-12-06","arxiv_id":"2212.02782","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-metadata-inference-using-a","title":"Building Metadata Inference Using a Transducer Based Language Model","date":"2022-12-05","arxiv_id":"2212.01964","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-and-accurate-factorized-neural","title":"Fast and accurate factorized neural transducer for text adaption of end-to-end speech recognition models","date":"2022-12-05","arxiv_id":"2212.01992","repositories_listed":0,"syntology":null},{"url":null,"slug":"i2mvformer-large-language-model-generated","title":"I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification","date":"2022-12-05","arxiv_id":"2212.02291","repositories_listed":0,"syntology":null},{"url":null,"slug":"legal-prompt-engineering-for-multilingual","title":"Legal Prompt Engineering for Multilingual Legal Judgement Prediction","date":"2022-12-05","arxiv_id":"2212.02199","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-fast-weight-language-models","title":"Meta-Learning Fast Weight Language Models","date":"2022-12-05","arxiv_id":"2212.02475","repositories_listed":0,"syntology":null},{"url":null,"slug":"milmo-minority-multilingual-pre-trained","title":"MiLMo:Minority Multilingual Pre-trained Language Model","date":"2022-12-04","arxiv_id":"2212.01779","repositories_listed":0,"syntology":null},{"url":"/paper/toward-efficient-language-model-pretraining","slug":"toward-efficient-language-model-pretraining","title":"Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE","date":"2022-12-04","arxiv_id":"2212.01853","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-memory-transformer-for-processing-long","title":"Global memory transformer for processing long documents","date":"2022-12-03","arxiv_id":"2212.01650","repositories_listed":0,"syntology":null},{"url":null,"slug":"compound-tokens-channel-fusion-for-vision","title":"Compound Tokens: Channel Fusion for Vision-Language Representation Learning","date":"2022-12-02","arxiv_id":"2212.01447","repositories_listed":0,"syntology":null},{"url":null,"slug":"faster-adaptive-federated-learning","title":"Faster Adaptive Federated Learning","date":"2022-12-02","arxiv_id":"2212.00974","repositories_listed":0,"syntology":null},{"url":null,"slug":"general-framework-for-self-supervised-model","title":"Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning","date":"2022-12-02","arxiv_id":"2212.01032","repositories_listed":0,"syntology":null},{"url":null,"slug":"legal-prompting-teaching-a-language-model-to","title":"Legal Prompting: Teaching a Language Model to Think Like a Lawyer","date":"2022-12-02","arxiv_id":"2212.01326","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapted-multimodal-bert-with-layer-wise","title":"Adapted Multimodal BERT with Layer-wise Fusion for Sentiment Analysis","date":"2022-12-01","arxiv_id":"2212.00678","repositories_listed":0,"syntology":null},{"url":null,"slug":"climedbert-a-pre-trained-language-model-for","title":"CliMedBERT: A Pre-trained Language Model for Climate and Health-related Text","date":"2022-12-01","arxiv_id":"2212.00689","repositories_listed":0,"syntology":null},{"url":null,"slug":"extensible-prompts-for-language-models","title":"Extensible Prompts for Language Models on Zero-shot Language Style Customization","date":"2022-12-01","arxiv_id":"2212.00616","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-model-pre-training-on-true-negatives-1","title":"Language Model Pre-training on True Negatives","date":"2022-12-01","arxiv_id":"2212.00460","repositories_listed":0,"syntology":null},{"url":null,"slug":"budgetlongformer-can-we-cheaply-pretrain-a","title":"BudgetLongformer: Can we Cheaply Pretrain a SotA Legal Language Model From Scratch?","date":"2022-11-30","arxiv_id":"2211.17135","repositories_listed":0,"syntology":null},{"url":null,"slug":"xtrimoabfold-improving-antibody-structure","title":"xTrimoABFold: De novo Antibody Structure Prediction without MSA","date":"2022-11-30","arxiv_id":"2212.00735","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-transcription-of-uk-supreme-court","title":"Better Transcription of UK Supreme Court Hearings","date":"2022-11-29","arxiv_id":"2211.17094","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-astrobert-using-semantic-textual","title":"Improving astroBERT using Semantic Textual Similarity","date":"2022-11-29","arxiv_id":"2212.00744","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-language-models-to-find-agreement","title":"Fine-tuning language models to find agreement among humans with diverse preferences","date":"2022-11-28","arxiv_id":"2211.15006","repositories_listed":0,"syntology":null},{"url":null,"slug":"inter-kd-intermediate-knowledge-distillation","title":"Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition","date":"2022-11-28","arxiv_id":"2211.15075","repositories_listed":0,"syntology":null},{"url":"/paper/large-pre-trained-models-with-extra-large","slug":"large-pre-trained-models-with-extra-large","title":"Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All","date":"2022-11-28","arxiv_id":"2211.15199","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-distance-metric-learning-for-few","title":"Revisiting Distance Metric Learning for Few-Shot Natural Language Classification","date":"2022-11-28","arxiv_id":"2211.15202","repositories_listed":0,"syntology":null},{"url":null,"slug":"detect-localize-repair-a-unified-framework","title":"Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5","date":"2022-11-27","arxiv_id":"2211.14875","repositories_listed":0,"syntology":null},{"url":null,"slug":"skdbert-compressing-bert-via-stochastic","title":"SKDBERT: Compressing BERT via Stochastic Knowledge Distillation","date":"2022-11-26","arxiv_id":"2211.14466","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-study-between-token-classification","title":"Comparison Study Between Token Classification and Sequence Classification In Text Classification","date":"2022-11-25","arxiv_id":"2211.13899","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-answering-and-question-generation","title":"Question Answering and Question Generation for Finnish","date":"2022-11-24","arxiv_id":"2211.13794","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-level-representation-from-bytes-for","title":"Word-Level Representation From Bytes For Language Modeling","date":"2022-11-23","arxiv_id":"2211.12677","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypertuning-toward-adapting-large-language","title":"HyperTuning: Toward Adapting Large Language Models without Back-propagation","date":"2022-11-22","arxiv_id":"2211.12485","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-prompting-for-few-shot-action","title":"Knowledge Prompting for Few-shot Action Recognition","date":"2022-11-22","arxiv_id":"2211.12030","repositories_listed":0,"syntology":null},{"url":"/paper/retrieval-augmented-multimodal-language","slug":"retrieval-augmented-multimodal-language","title":"Retrieval-Augmented Multimodal Language Modeling","date":"2022-11-22","arxiv_id":"2211.12561","repositories_listed":0,"syntology":null},{"url":null,"slug":"clipcrop-conditioned-cropping-driven-by","title":"ClipCrop: Conditioned Cropping Driven by Vision-Language Model","date":"2022-11-21","arxiv_id":"2211.11492","repositories_listed":0,"syntology":null},{"url":null,"slug":"deanthropomorphising-nlp-can-a-language-model","title":"Deanthropomorphising NLP: Can a Language Model Be Conscious?","date":"2022-11-21","arxiv_id":"2211.11483","repositories_listed":0,"syntology":null},{"url":null,"slug":"embracing-ambiguity-improving-similarity","title":"Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge","date":"2022-11-20","arxiv_id":"2211.10997","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-per-image-token-consistency-for","title":"Leveraging per Image-Token Consistency for Vision-Language Pre-training","date":"2022-11-20","arxiv_id":"2211.15398","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-human-motion-generation-from-the-text-via","title":"3d human motion generation from the text via gesture action classification and the autoregressive model","date":"2022-11-18","arxiv_id":"2211.10003","repositories_listed":0,"syntology":null},{"url":null,"slug":"metadata-might-make-language-models-better","title":"Metadata Might Make Language Models Better","date":"2022-11-18","arxiv_id":"2211.10086","repositories_listed":0,"syntology":null},{"url":null,"slug":"longfnt-long-form-speech-recognition-with","title":"LongFNT: Long-form Speech Recognition with Factorized Neural Transducer","date":"2022-11-17","arxiv_id":"2211.09412","repositories_listed":0,"syntology":null},{"url":null,"slug":"planning-with-large-language-models-via","title":"CAPE: Corrective Actions from Precondition Errors using Large Language Models","date":"2022-11-17","arxiv_id":"2211.09935","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompting-palm-for-translation-assessing","title":"Prompting PaLM for Translation: Assessing Strategies and Performance","date":"2022-11-16","arxiv_id":"2211.09102","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-computationally-verifiable-semantic","title":"Towards Computationally Verifiable Semantic Grounding for Language Models","date":"2022-11-16","arxiv_id":"2211.09070","repositories_listed":0,"syntology":null},{"url":null,"slug":"tsmind-alibaba-and-soochow-university-s","title":"TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task","date":"2022-11-16","arxiv_id":"2211.08987","repositories_listed":0,"syntology":null},{"url":null,"slug":"ed-faith-evaluating-dialogue-summarization-on","title":"ED-FAITH: Evaluating Dialogue Summarization on Faithfulness","date":"2022-11-15","arxiv_id":"2211.08464","repositories_listed":0,"syntology":null},{"url":null,"slug":"empowering-language-models-with-knowledge","title":"Empowering Language Models with Knowledge Graph Reasoning for Question Answering","date":"2022-11-15","arxiv_id":"2211.08380","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-semantics-into-speech-encoders","title":"Introducing Semantics into Speech Encoders","date":"2022-11-15","arxiv_id":"2211.08402","repositories_listed":0,"syntology":null},{"url":null,"slug":"reasoning-circuits-few-shot-multihop-question","title":"Reasoning Circuits: Few-shot Multihop Question Generation with Structured Rationales","date":"2022-11-15","arxiv_id":"2211.08466","repositories_listed":0,"syntology":null},{"url":null,"slug":"relationship-of-the-language-distance-to","title":"Relationship of the language distance to English ability of a country","date":"2022-11-15","arxiv_id":"2211.07855","repositories_listed":0,"syntology":null},{"url":null,"slug":"robbert-2022-updating-a-dutch-language-model","title":"RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use","date":"2022-11-15","arxiv_id":"2211.08192","repositories_listed":0,"syntology":null},{"url":null,"slug":"albert-with-knowledge-graph-encoder-utilizing","title":"ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering","date":"2022-11-14","arxiv_id":"2211.07065","repositories_listed":0,"syntology":null},{"url":null,"slug":"grafting-pre-trained-models-for-multimodal","title":"Grafting Pre-trained Models for Multimodal Headline Generation","date":"2022-11-14","arxiv_id":"2211.07210","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-mathematics-formalisation-assistant","title":"Towards a Mathematics Formalisation Assistant using Large Language Models","date":"2022-11-14","arxiv_id":"2211.07524","repositories_listed":0,"syntology":null},{"url":null,"slug":"textual-data-augmentation-for-patient","title":"Textual Data Augmentation for Patient Outcomes Prediction","date":"2022-11-13","arxiv_id":"2211.06778","repositories_listed":0,"syntology":null},{"url":null,"slug":"docut5-seq2seq-sql-generation-with-table","title":"DocuT5: Seq2seq SQL Generation with Table Documentation","date":"2022-11-11","arxiv_id":"2211.06193","repositories_listed":0,"syntology":null},{"url":null,"slug":"bert-in-plutarch-s-shadows","title":"BERT in Plutarch's Shadows","date":"2022-11-10","arxiv_id":"2211.05673","repositories_listed":0,"syntology":null},{"url":null,"slug":"formlm-recommending-creation-ideas-for-online","title":"FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information","date":"2022-11-10","arxiv_id":"2211.05284","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-learning-for-domain-adaptation-in-task","title":"Prompt Learning for Domain Adaptation in Task-Oriented Dialogue","date":"2022-11-10","arxiv_id":"2211.05596","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-cringe-loss-learning-what-language-not-to","title":"The CRINGE Loss: Learning what language not to model","date":"2022-11-10","arxiv_id":"2211.05826","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-multi-corpora-language-model","title":"Adaptive Multi-Corpora Language Model Training for Speech Recognition","date":"2022-11-09","arxiv_id":"2211.05121","repositories_listed":0,"syntology":null},{"url":"/paper/ernie-unix2-a-unified-cross-lingual-cross","slug":"ernie-unix2-a-unified-cross-lingual-cross","title":"ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation","date":"2022-11-09","arxiv_id":"2211.04861","repositories_listed":0,"syntology":null},{"url":null,"slug":"ff2-a-feature-fusion-two-stream-framework-for","title":"FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration","date":"2022-11-09","arxiv_id":"2211.04699","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-noisy-student-training-on-non","title":"Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition","date":"2022-11-09","arxiv_id":"2211.04717","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-self-supervised-peptide-sequence","title":"Training self-supervised peptide sequence models on artificially chopped proteins","date":"2022-11-09","arxiv_id":"2211.06428","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-cross-modal-interactions-in-v-l","title":"Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions","date":"2022-11-09","arxiv_id":"2211.04971","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-with-tabular-language-models","title":"Active Learning with Tabular Language Models","date":"2022-11-08","arxiv_id":"2211.04128","repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-and-data-efficient-continual-pre","title":"Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic","date":"2022-11-08","arxiv_id":"2211.03966","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-conditioned-embedding-diffusion-for-text","title":"Self-conditioned Embedding Diffusion for Text Generation","date":"2022-11-08","arxiv_id":"2211.04236","repositories_listed":0,"syntology":null},{"url":null,"slug":"complex-reading-comprehension-through","title":"Complex Reading Comprehension Through Question Decomposition","date":"2022-11-07","arxiv_id":"2211.03277","repositories_listed":0,"syntology":null},{"url":"/paper/probing-neural-language-models-for","slug":"probing-neural-language-models-for","title":"Probing neural language models for understanding of words of estimative probability","date":"2022-11-07","arxiv_id":"2211.03358","repositories_listed":0,"syntology":null},{"url":null,"slug":"prompter-utilizing-large-language-model","title":"Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following","date":"2022-11-07","arxiv_id":"2211.03267","repositories_listed":0,"syntology":null},{"url":null,"slug":"noisy-channel-for-automatic-text","title":"Noisy Channel for Automatic Text Simplification","date":"2022-11-06","arxiv_id":"2211.03152","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-statistical-representations-for-end","title":"Probing Statistical Representations For End-To-End ASR","date":"2022-11-03","arxiv_id":"2211.01993","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-large-pre-trained-language-model-to","title":"Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device","date":"2022-11-03","arxiv_id":"2212.01217","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-training-can-improve","title":"Generative Adversarial Training Can Improve Neural Language Models","date":"2022-11-02","arxiv_id":"2211.09728","repositories_listed":0,"syntology":null},{"url":null,"slug":"internal-language-model-estimation-based-1","title":"Internal Language Model Estimation based Adaptive Language Model Fusion for Domain Adaptation","date":"2022-11-02","arxiv_id":"2211.00968","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-distillation-of-semantic","title":"Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model","date":"2022-11-02","arxiv_id":"2211.01200","repositories_listed":0,"syntology":null},{"url":null,"slug":"numerical-optimizations-for-weighted-low-rank","title":"Numerical Optimizations for Weighted Low-rank Estimation on Language Model","date":"2022-11-02","arxiv_id":"2211.09718","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-zero-shot-code-switched-speech","title":"Towards Zero-Shot Code-Switched Speech Recognition","date":"2022-11-02","arxiv_id":"2211.01458","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-quantitative-analysis-of-comparison-of","title":"A Quantitative Analysis of Comparison of Emoji Sentiment: Taiwan Mandarin Users and English Users","date":"2022-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hantrans-an-empirical-study-on-cross-era","title":"HanTrans: An Empirical Study on Cross-Era Transferability of Chinese Pre-trained Language Model","date":"2022-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"language-model-based-chinese-handwriting","title":"Language Model Based Chinese Handwriting Address Recognition","date":"2022-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-can-guide-experimental","title":"Machine learning can guide experimental approaches for protein digestibility estimations","date":"2022-11-01","arxiv_id":"2211.00625","repositories_listed":0,"syntology":null},{"url":null,"slug":"nerve-at-rocling-2022-shared-task-a","title":"NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach","date":"2022-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"4de7025f06791ecbbecfbcf84e15bfda13df7fd7c682b062c8e6e4bc944a6710","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}