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Found 13 papers, 9 papers with code
How Do Seq2Seq Models Perform on End-to-End Data-to-Text Generation?
With the rapid development of deep learning, Seq2Seq paradigm has become prevalent for end-to-end data-to-text generation, and the BLEU scores have been increasing in recent years.
Understanding the Interplay between Parametric and Contextual Knowledge for Large Language Models
To support this investigation, we introduce ECHOQA, a benchmark spanning scientific, factual, and commonsense knowledge.
Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement
The rapid advancement of large language models (LLMs) has significantly enhanced the capabilities of AI-driven agents across various tasks.
Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability
The evaluation of natural language generation (NLG) tasks is a significant and longstanding research area.
MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency
Our benchmark facilitates independent correction of misreading and misrecognition errors by editing the corresponding knowledge component.
ContraSolver: Self-Alignment of Language Models by Resolving Internal Preference Contradictions
While substantial advancements have been made in developing large language models (LLMs), achieving control over their behavior can be difficult.
EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image Captioning
News image captioning requires model to generate an informative caption rich in entities, with the news image and the associated news article.
Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluation
In recent years, substantial advancements have been made in the development of large language models, achieving remarkable performance across diverse tasks.
History Matters: Temporal Knowledge Editing in Large Language Model
The imperative task of revising or updating the knowledge stored within large language models arises from two distinct sources: intrinsic errors inherent in the model which should be corrected and outdated knowledge due to external shifts in the real world which should be updated.
ALCUNA: Large Language Models Meet New Knowledge
With the rapid development of NLP, large-scale language models (LLMs) excel in various tasks across multiple domains now.
A Comprehensive Evaluation and Analysis Study for Chinese Spelling Check
With the development of pre-trained models and the incorporation of phonetic and graphic information, neural models have achieved high scores in Chinese Spelling Check (CSC).
Human-like Summarization Evaluation with ChatGPT
Evaluating text summarization is a challenging problem, and existing evaluation metrics are far from satisfactory.
Error-Robust Retrieval for Chinese Spelling Check
Chinese Spelling Check (CSC) aims to detect and correct error tokens in Chinese contexts, which has a wide range of applications.
