Integrating large language models (LLMs) with various tools has led to increased attention
in the field. Existing approaches either involve fine-tuning the LLM, which is both …

Large language models have demonstrated outstanding performance on a wide range of
tasks such as question answering and code generation. On a high level, given an input, a …

Abstract Knowledge plays a critical role in artificial intelligence. Recently, the extensive
success of pre-trained language models (PLMs) has raised significant attention about how …

Highly effective, task-specific prompts are often heavily engineered by experts to integrate
detailed instructions and domain insights based on a deep understanding of both instincts of …

Large Language Models (LLMs) have shown remarkable performances on a wide range of
natural language understanding and generation tasks. We observe that the LLMs provide …

In this paper, we present a large-scale evaluation probing GPT-4V's capabilities and
limitations for biomedical image analysis. GPT-4V represents a breakthrough in artificial …

We demonstrate that, through appropriate prompting, GPT-3 family of models can be
triggered to perform iterative behaviours necessary to execute (rather than just write or …

Language models (LMs) can perform complex reasoning either end-to-end, with hidden
latent state, or compositionally, with transparent intermediate state. Composition offers …

Spatial reasoning over text is challenging as the models not only need to extract the direct
spatial information from the text but also reason over those and infer implicit spatial relations …

Many recent prompting strategies for large language models (LLMs) query the model
multiple times sequentially--first to produce intermediate results and then the final answer …