For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …

Like humans, large language models (LLMs) do not always generate the best output on their
first try. Motivated by how humans refine their written text, we introduce Self-Refine, an …

The introduction of large language models has significantly advanced code generation.
However, open-source models often lack the execution capabilities and iterative refinement …

Language models (LMs) have recently shown remarkable performance on reasoning tasks
by explicitly generating intermediate inferences, eg, chain-of-thought prompting. However …

Natural language generation has witnessed significant advancements due to the training of
large language models on vast internet-scale datasets. Despite these advancements, there …

Despite their unprecedented success, even the largest language models make mistakes.
Similar to how humans learn and improve using feedback, previous work proposed …

Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to
humans. For example, GPT-3 would mistakenly interpret" What word is similar to good?" to …

AI assistants, such as ChatGPT, are being increasingly used by students in higher education
institutions. While these tools provide opportunities for improved teaching and education …

Editing model parameters directly in Transformers makes updating open-source transformer-
based models possible without re-training (Meng et al., 2023). However, these editing …

Many approaches to Natural Language Processing (NLP) tasks often treat them as single-
step problems, where an agent receives an instruction, executes it, and is evaluated based …