Survey of hallucination in natural language generation
Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …
the development of sequence-to-sequence deep learning technologies such as Transformer …
Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models
Z Lin, S Guan, W Zhang, H Zhang, Y Li… - Artificial Intelligence …, 2024 - Springer
Recently, large language models (LLMs) have attracted considerable attention due to their
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …
remarkable capabilities. However, LLMs' generation of biased or hallucinatory content …
A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …
natural language processing (NLP), fueling a paradigm shift in information acquisition …
Biases in large language models: origins, inventory, and discussion
In this article, we introduce and discuss the pervasive issue of bias in the large language
models that are currently at the core of mainstream approaches to Natural Language …
models that are currently at the core of mainstream approaches to Natural Language …
Chatgpt as a factual inconsistency evaluator for text summarization
The performance of text summarization has been greatly boosted by pre-trained language
models. A main concern of existing methods is that most generated summaries are not …
models. A main concern of existing methods is that most generated summaries are not …
Hallucination is inevitable: An innate limitation of large language models
Hallucination has been widely recognized to be a significant drawback for large language
models (LLMs). There have been many works that attempt to reduce the extent of …
models (LLMs). There have been many works that attempt to reduce the extent of …
AlignScore: Evaluating factual consistency with a unified alignment function
Many text generation applications require the generated text to be factually consistent with
input information. Automatic evaluation of factual consistency is challenging. Previous work …
input information. Automatic evaluation of factual consistency is challenging. Previous work …
Logiqa 2.0—an improved dataset for logical reasoning in natural language understanding
NLP research on logical reasoning regains momentum with the recent releases of a handful
of datasets, notably LogiQA and Reclor. Logical reasoning is exploited in many probing …
of datasets, notably LogiQA and Reclor. Logical reasoning is exploited in many probing …
Trueteacher: Learning factual consistency evaluation with large language models
Factual consistency evaluation is often conducted using Natural Language Inference (NLI)
models, yet these models exhibit limited success in evaluating summaries. Previous work …
models, yet these models exhibit limited success in evaluating summaries. Previous work …
Faithfulness in natural language generation: A systematic survey of analysis, evaluation and optimization methods
Natural Language Generation (NLG) has made great progress in recent years due to the
development of deep learning techniques such as pre-trained language models. This …
development of deep learning techniques such as pre-trained language models. This …