← Learning Types
Machine Learning › Learning Types ›

Evaluation

1654 directly classified papers

Papers per year

Papers

Towards General Error Diagnosis via Behavioral Testing in Machine Translation EMNLP 2023
Evaluating Verifiability in Generative Search Engines EMNLP 2023
VIPHY: Probing “Visible” Physical Commonsense Knowledge EMNLP 2023
“You Are An Expert Linguistic Annotator”: Limits of LLMs as Analyzers of Abstract Meaning Representation EMNLP 2023
USB: A Unified Summarization Benchmark Across Tasks and Domains EMNLP 2023
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation EMNLP 2023
CCEval: A Representative Evaluation Benchmark for the Chinese-centric Multilingual Machine Translation EMNLP 2023
Automatic Analysis of Substantiation in Scientific Peer Reviews EMNLP 2023
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark EMNLP 2023
Debias NLU Datasets via Training-free Perturbations EMNLP 2023
Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate EMNLP 2023
Probing the “Creativity” of Large Language Models: Can models produce divergent semantic association? EMNLP 2023
On Event Individuation for Document-Level Information Extraction EMNLP 2023
TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks EMNLP 2023
Annotation Sensitivity: Training Data Collection Methods Affect Model Performance EMNLP 2023
NarrativeXL: a Large-scale Dataset for Long-Term Memory Models EMNLP 2023
SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency EMNLP 2023
Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic EMNLP 2023
Predicting Question-Answering Performance of Large Language Models through Semantic Consistency EMNLP 2023
Separating form and meaning: Using self-consistency to quantify task understanding across multiple senses EMNLP 2023
To Burst or Not to Burst: Generating and Quantifying Improbable Text EMNLP 2023
Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs EMNLP 2023
Evaluating Neural Language Models as Cognitive Models of Language Acquisition EMNLP 2023
Walking a Tightrope – Evaluating Large Language Models in High-Risk Domains EMNLP 2023
mSCAN: A Dataset for Multilingual Compositional Generalisation Evaluation EMNLP 2023