We present symbol tuning-finetuning language models on in-context input-label pairs where
natural language labels (eg," positive/negative sentiment") are replaced with arbitrary …

Query rewriting refers to an established family of approaches that are applied to
underspecified and ambiguous queries to overcome the vocabulary mismatch problem in …

In this paper, we propose FinVis-GPT, a novel multimodal large language model (LLM)
specifically designed for financial chart analysis. By leveraging the power of LLMs and …

Large Language Models (LLMs) can learn new tasks through in-context supervised learning
(ie, ICL). This work studies if this ability extends to in-context reinforcement learning (ICRL) …

At the beginning era of large language model, it is quite critical to generate a high-quality
financial dataset to fine-tune a large language model for financial related tasks. Thus, this …

In recent years, with the rise of various machine learning methods, the Ultraviolet and Near
Infrared (UV-NIR) spectral analysis has been impressive in the determination of intricate …

Large language models (LLMs) are able to solve various tasks with only a few
demonstrations utilizing their in-context learning (ICL) abilities. However, LLMs often rely on …

Abstract Large Language Models (LLMs) have provided a new pathway for Named Entity
Recognition (NER) tasks. Compared with fine-tuning, LLM-powered prompting methods …

The relationship between language and thought remains an unresolved philosophical issue.
Existing viewpoints can be broadly categorized into two schools: one asserting their …

The advances in large language models have been noticeable to researchers and the
general public in recent times [56]. We see the development of large language models in …