7 accepted papers
Recent advances in Large Language Models(LLMs) have led to remarkable achievements across a variety of Natural Language Processing(NLP) tasks, making prompt engineering increasingly central to guiding model outputs. While manual methods (e.g., “chain-of-thought,” “step-by-step” prompts) can be effec…
Designing optimal prompts for Large Language Models (LLMs) is a complex and resource-intensive task, often requiring substantial human expertise. Existing approaches typically separate the optimization of prompt instructions and in-context learning examples, leading to incohesive, suboptimal results…
Advancements in Large Vision-Language Models (LVLMs) have demonstrated impressive performance in image-conditioned text generation; however, hallucinated outputs–text that misaligns with the visual input–pose a major barrier to their use in safety-critical applications. We introduce ConfLVLM, a conf
Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often fail under the strict definition of membership based on exact matching, and have suggested relaxing this definition to i…
Evaluating the quality and consistency of text generated by Large Language Models (LLMs) poses a significant, yet unresolved challenge for industry research. We propose , an automated framework for evaluating and improving the consistency of LLM-generated texts using a divide-conquer-reasoning appro…
Language models (LMs) are known to suffer from hallucinations and misinformation. Retrieval augmented generation (RAG) that retrieves verifiable information from an external knowledge corpus to complement the parametric knowledge in LMs provides a tangible solution to these problems. However, the ge…
Hallucination detection is a critical step toward understanding the trustworthiness of modern language models (LMs). To achieve this goal, we re-examine existing detection approaches based on the self-consistency of LMs and uncover two types of hallucinations resulting from 1) question-level and 2)…