Ep. 253 - June 3, 2024

An episode of the TechcraftingAI NLP podcast, hosted by Brad Edwards, titled "Ep. 253 - June 3, 2024" was published on June 4, 2024 and runs 70 minutes.

June 4, 2024 ·70m · TechcraftingAI NLP

Summary

ArXiv NLP research for Monday, June 03, 2024. 00:19: Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost 01:38: Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer 03:06: Selectively Answering Visual Questions 04:11: Take its Essence, Discard its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect 05:36: Predicting Drug-Gene Relations via Analogy Tasks with Word Embeddings 06:51: SemCoder: Training Code Language Models with Comprehensive Semantics 08:39: Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration 10:26: Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels 11:45: Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors 13:26: Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs 14:34: MACT: Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing 15:48: Guiding ChatGPT to Generate Salient Domain Summaries 17:51: Synergizing Unsupervised and Supervised Learning: A Hybrid Approach for Accurate Natural Language Task Modeling 19:30: TCMBench: A Comprehensive Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine 21:38: Explore then Determine: A GNN-LLM Synergy Framework for Reasoning over Knowledge Graph 22:51: Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization 24:08: Are AI-Generated Text Detectors Robust to Adversarial Perturbations? 25:42: Automatic Essay Multi-dimensional Scoring with Fine-tuning and Multiple Regression 26:35: Improving Pseudo Labels with Global-Local Denoising Framework for Cross-lingual Named Entity Recognition 28:01: Demonstration Augmentation for Zero-shot In-context Learning 29:31: EffiQA: Efficient Question-Answering with Strategic Multi-Model Collaboration on Knowledge Graphs 31:05: Towards Scalable Automated Alignment of LLMs: A Survey 32:19: EduNLP: Towards a Unified and Modularized Library for Educational Resources 33:44: Focus on the Core: Efficient Attention via Pruned Token Compression for Document Classification 35:07: Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses 36:36: When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs 37:58: CodeR: Issue Resolving with Multi-Agent and Task Graphs 38:54: Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding 40:10: FactGenius: Combining Zero-Shot Prompting and Fuzzy Relation Mining to Improve Fact Verification with Knowledge Graphs 41:27: Probing Language Models for Pre-training Data Detection 42:45: R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models 44:32: Privacy in LLM-based Recommendation: Recent Advances and Future Directions 45:23: Linguistic Analysis, Description, and Typological Exploration with Categorial Grammar (TheBench Guide) 46:52: D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models 48:52: Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function 50:07: Sparsity-Accelerated Training for Large Language Models 51:36: Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study 53:34: Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models 54:42: LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translation 55:55: Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach 57:10: Understanding Token Probability Encoding in Output Embeddings

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