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Researchers from the University of Washington and Meta AI Present a Simple Context-Aware Decoding (CAD) Method to Encourage the Language Model to Attend to Its Context During Generation
Language models (LMs) have shown remarkable effectiveness in generating coherent and fluent continuations of a prompt or document prefix. However, they often struggle to balance prior knowledge learned during pretraining and context knowledge provided in the input context, leading to unfaithful or hallucinatory text generation.
Context-Aware Decoding (CAD) Solution
Researchers have introduced Context-Aware Decoding (CAD) as a practical solution to address these challenges. CAD follows a contrastive output distribution that amplifies the difference between the output probabilities when a model is used with and without context. This encourages the LM to pay sufficient attention to its context during generation, leading to substantial improvements in tasks where resolving the knowledge conflict is essential.
CAD effectively downweights the prior knowledge when more relevant contextual information is provided, without requiring additional training. It can be used with off-the-shelf pre-trained LMs and has been experimentally shown to outperform standard decoding algorithms across various datasets and LM families, including OPT, GPT, LLaMA, and FLAN-T5 for summarization tasks.
When applied to LLAMA30B in CNN-DM, CAD led to a 21% increase in ROUGE-L, a 14.3% increase in factKB, and a 7.8% increase in BERT-P, demonstrating its ability to improve the quality and factuality of generated summaries.
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