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The Expresssive Power of Transformers with Chain of Thought. (arXiv:2310.07923v1 [cs.LG]) 

Pit One Against Many: Leveraging Attention-head Embeddings for Parameter-efficient Multi-head Attention. (arXiv:2310.07911v1 [cs.CL]) 

LangNav: Language as a Perceptual Representation for Navigation. (arXiv:2310.07889v1 [cs.CV]) 

TabLib: A Dataset of 627M Tables with Context. (arXiv:2310.07875v1 [cs.CL]) 

Assessing Evaluation Metrics for Neural Test Oracle Generation. (arXiv:2310.07856v1 [cs.CL]) 

Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations. (arXiv:2310.07849v1 [cs.CL]) 

Framework for Question-Answering in Sanskrit through Automated Construction of Knowledge Graphs. (arXiv:2310.07848v1 [cs.CL]) 

Does Synthetic Data Make Large Language Models More Efficient?. (arXiv:2310.07830v1 [cs.CL]) 

Antarlekhaka: A Comprehensive Tool for Multi-task Natural Language Annotation. (arXiv:2310.07826v1 [cs.CL]) 

Non-autoregressive Text Editing with Copy-aware Latent Alignments. (arXiv:2310.07821v1 [cs.CL]) 

Faithfulness Measurable Masked Language Models. (arXiv:2310.07819v1 [cs.CL]) 

Exploring the Relationship between Analogy Identification and Sentence Structure Encoding in Large Language Models. (arXiv:2310.07818v1 [cs.CL]) 

Language Models As Semantic Indexers. (arXiv:2310.07815v1 [cs.IR]) 

A general mechanism of humor: reformulating the semantic overlap. (arXiv:2310.07803v1 [cs.CL]) 

Ontology Enrichment for Effective Fine-grained Entity Typing. (arXiv:2310.07795v1 [cs.CL]) 

GenTKG: Generative Forecasting on Temporal Knowledge Graph. (arXiv:2310.07793v1 [cs.CL]) 

Jaynes Machine: The universal microstructure of deep neural networks. (arXiv:2310.06960v1 [cond-mat.stat-mech] CROSS LISTED) 

PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration. (arXiv:2309.13869v1 [cs.CL] CROSS LISTED) 

Well Begun is Half Done: Generator-agnostic Knowledge Pre-Selection for Knowledge-Grounded Dialogue. (arXiv:2310.07659v2 [cs.CL] UPDATED) 

Rethinking the BERT-like Pretraining for DNA Sequences. (arXiv:2310.07644v2 [cs.AI] UPDATED) 

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