Language Model Generation

Language model generation focuses on creating human-quality text using artificial intelligence, aiming to improve fluency, coherence, and alignment with user intent. Current research emphasizes enhancing controllability (e.g., through prompt compression and contrastive conditioning), mitigating issues like copyright infringement and toxicity, and improving evaluation methods (e.g., by considering both literal and non-literal copying, and developing benchmarks with nuanced difficulty levels). These advancements are crucial for building trustworthy and reliable language models with broad applications in various fields, from question answering and dialogue systems to code generation and creative writing.