Add to ORKGLarge language models (LLMs) have been shown to possess impressive capabilities, while also raising crucial concerns about the faithfulness of their responses. A primary issue arising in this context is the management of (un)answerable queries by LLMs, which often results in hallucinatory behavior due to overconfidence. In this paper, we explore the behavior of LLMs when presented with (un)answerable queries. We ask: do models represent the fact that the question is (un)answerable when generating a hallucinatory answer? Our results show strong indications that such models encode the answerability of an input query, with the representation of the first decoded token often being a strong indicator. These findings shed new light on the spatial...
When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last w...
Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truth...
We study whether language models can evaluate the validity of their own claims and predict which que...
Large language models (LLMs) have demonstrated impressive language understanding and generation capa...
Semantic consistency of a language model is broadly defined as the model's ability to produce semant...
Current large language models (LLMs) can exhibit near-human levels of performance on many natural la...
Although remarkable progress has been achieved in preventing large language model (LLM) hallucinatio...
Though state-of-the-art (SOTA) NLP systems have achieved remarkable performance on a variety of lang...
Trustworthy language models should abstain from answering questions when they do not know the answer...
We propose a benchmark to measure whether a language model is truthful in generating answers to ques...
Large Language Models (LLMs), such as ChatGPT/GPT-4, have garnered widespread attention owing to the...
Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are s...
The interactive nature of Large Language Models (LLMs) theoretically allows models to refine and imp...
The fluency and creativity of large pre-trained language models (LLMs) have led to their widespread ...
The development of highly fluent large language models (LLMs) has prompted increased interest in ass...
When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last w...
Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truth...
We study whether language models can evaluate the validity of their own claims and predict which que...
Large language models (LLMs) have demonstrated impressive language understanding and generation capa...
Semantic consistency of a language model is broadly defined as the model's ability to produce semant...
Current large language models (LLMs) can exhibit near-human levels of performance on many natural la...
Although remarkable progress has been achieved in preventing large language model (LLM) hallucinatio...
Though state-of-the-art (SOTA) NLP systems have achieved remarkable performance on a variety of lang...
Trustworthy language models should abstain from answering questions when they do not know the answer...
We propose a benchmark to measure whether a language model is truthful in generating answers to ques...
Large Language Models (LLMs), such as ChatGPT/GPT-4, have garnered widespread attention owing to the...
Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are s...
The interactive nature of Large Language Models (LLMs) theoretically allows models to refine and imp...
The fluency and creativity of large pre-trained language models (LLMs) have led to their widespread ...
The development of highly fluent large language models (LLMs) has prompted increased interest in ass...
When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last w...
Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truth...
We study whether language models can evaluate the validity of their own claims and predict which que...