Resources & Methods

Exploring the Best Practices of Query Expansion with Large Language Models EMNLP 2024 Exploring the Factual Consistency in Dialogue Comprehension of Large Language Models NAACL 2024 Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data NAACL 2024 Exploring the Potential of Large Foundation Models for Open-Vocabulary HOI Detection CVPR 2024 Exploring the Potential of Large Language Models in Computational Argumentation ACL 2024 Exploring the Potential of Large Language Models (LLMs) for Low-resource Languages: A Study on Named-Entity Recognition (NER) and Part-Of-Speech (POS) Tagging for Nepali Language COLING 2024 Exploring the Potential of Multimodal LLM with Knowledge-Intensive Multimodal ASR EMNLP 2024 Exploring the Practicality of Generative Retrieval on Dynamic Corpora EMNLP 2024 Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh’s Submission to AmericasNLP 2024 Translation Task NAACL 2024 Extending Context Window of Large Language Models via Semantic Compression ACL 2024 Extending the BabyLM Initiative : Promoting Diversity in Datasets and Metrics through High-Quality Linguistic Corpora CoNLL 2024 Extending VerbNet’s Verb-Specific Features to Enhance Selectional Preferences of Semantic Roles COLING 2024 Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction EMNLP 2024 Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation ACL 2024 Extracting and Summarizing Evidence of Suicidal Ideation in Social Media Contents Using Large Language Models EACL 2024 Extracting Epilepsy Patient Data with Llama 2 ACL 2024 Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods COLING 2024 FAC2E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition EMNLP 2024 FactAlign: Fact-Level Hallucination Detection and Classification Through Knowledge Graph Alignment NAACL 2024 FactAlign: Long-form Factuality Alignment of Large Language Models EMNLP 2024 Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models ACL 2024 Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers EMNLP 2024 Factual Confidence of LLMs: on Reliability and Robustness of Current Estimators ACL 2024 Factuality of Large Language Models: A Survey EMNLP 2024 FaGANet: An Evidence-Based Fact-Checking Model with Integrated Encoder Leveraging Contextual Information COLING 2024