In-context Learning and Gradient Descent Revisited. (arXiv:2311.07772v1 [cs.CL])
GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization. (arXiv:2311.07767v1 [cs.CL])
Vision-Language Integration in Multimodal Video Transformers (Partially) Aligns with the Brain. (arXiv:2311.07766v1 [cs.CV])
Generalization Analogies (GENIES): A Testbed for Generalizing AI Oversight to Hard-To-Measure Domains. (arXiv:2311.07723v1 [cs.AI])
PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature. (arXiv:2311.07715v1 [cs.CL])
Measuring Entrainment in Spontaneous Code-switched Speech. (arXiv:2311.07703v1 [cs.CL])
AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising. (arXiv:2311.07700v1 [cs.CL])
On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models. (arXiv:2311.07692v1 [cs.LG])
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming. (arXiv:2311.07689v1 [cs.CL])
Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games. (arXiv:2311.07687v1 [cs.CL])
Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion. (arXiv:2311.07682v1 [cs.CL])
Past as a Guide: Leveraging Retrospective Learning for Python Code Completion. (arXiv:2311.07635v1 [cs.SE])
Large Language Models' Understanding of Math: Source Criticism and Extrapolation. (arXiv:2311.07618v1 [cs.LG])
CLAMP: A Contrastive Language And Molecule Pre-training Network. (arXiv:2311.07617v1 [cs.CL])
Intentional Biases in LLM Responses. (arXiv:2311.07611v1 [cs.CL])
Multi-Label Topic Model for Financial Textual Data. (arXiv:2311.07598v1 [q-fin.ST])
How to Bridge the Gap between Modalities: A Comprehensive Survey on Multimodal Large Language Model. (arXiv:2311.07594v1 [cs.CL])
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification. (arXiv:2311.07593v1 [cs.CL])
Hallucination-minimized Data-to-answer Framework for Financial Decision-makers. (arXiv:2311.07592v1 [cs.CL])
Identification of Books That are Suitable for Middle School Students Using Artificial Neural Networks. (arXiv:2311.07591v1 [cs.CL])
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