BLooP: Zero-Shot Abstractive Summarization Using Large Language Models with Bigram Lookahead Promotion

Abstract

Abstractive summarization requires models to generate summaries that convey information in the source document. While large language models can generate summaries without fine-tuning, they often miss key details and include extraneous information. We propose BLooP (Bigram Lookahead Promotion), a simple training-free decoding intervention that encourages large language models (LLMs) to generate tokens that form bigrams from the source document. BLooP operates through a hash table lookup at each decoding step, requiring no training, fine-tuning, or model modification. We demonstrate improvements in ROUGE and BARTScore for [Llama‑3.1‑8B‑Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct), [Mistral‑Nemo‑Instruct‑2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407), and [Gemma‑2‑9B‑IT](https://huggingface.co/google/gemma-2-9b-it) on CNN/DM, CCSum, Multi-News, and SciTLDR. Human evaluation shows that BLooP significantly improves faithfulness without reducing readability. We make the code available [here](https://github.com/varuniyer/BLooP).

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