Show newer

Large Language Models are Zero-Shot Reasoners. (arXiv:2205.11916v4 [cs.CL] UPDATED) 

AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks. (arXiv:2205.00305v4 [cs.CL] UPDATED) 

Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing. (arXiv:2201.11374v2 [cs.CL] UPDATED) 

SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising. (arXiv:2105.07911v2 [cs.CL] UPDATED) 

Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model. (arXiv:2301.12343v1 [cs.SD]) 

Time out of Mind: Generating Emotionally Conditioned Rate of Speech. (arXiv:2301.12331v1 [cs.CL]) 

Progressive Prompts: Continual Learning for Language Models. (arXiv:2301.12314v1 [cs.CL]) 

MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization. (arXiv:2301.12307v1 [cs.CL]) 

Presence of informal language, such as emoticons, hashtags, and slang, impact the performance of sentiment analysis models on social media text?. (arXiv:2301.12303v1 [cs.CL]) 

Semantic Parsing for Conversational Question Answering over Knowledge Graphs. (arXiv:2301.12217v1 [cs.CL]) 

Semantic Tagging with LSTM-CRF. (arXiv:2301.12206v1 [cs.CL]) 

Multilingual Sentence Transformer as A Multilingual Word Aligner. (arXiv:2301.12140v1 [cs.CL]) 

Bipol: Multi-axes Evaluation of Bias with Explainability in Benchmark Datasets. (arXiv:2301.12139v1 [cs.CL]) 

Underwater Robotics Semantic Parser Assistant. (arXiv:2301.12134v1 [cs.CL]) 

AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning. (arXiv:2301.12132v1 [cs.CL]) 

On Pre-trained Language Models for Antibody. (arXiv:2301.12112v1 [cs.CL]) 

Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples. (arXiv:2301.12074v1 [cs.CL]) 

Do Embodied Agents Dream of Pixelated Sheep?: Embodied Decision Making using Language Guided World Modelling. (arXiv:2301.12050v1 [cs.LG]) 

Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases. (arXiv:2301.12017v1 [cs.CL]) 

Improved knowledge distillation by utilizing backward pass knowledge in neural networks. (arXiv:2301.12006v1 [cs.LG]) 

Show older
Qoto Mastodon

QOTO: Question Others to Teach Ourselves
An inclusive, Academic Freedom, instance
All cultures welcome.
Hate speech and harassment strictly forbidden.