Large language models (LLMs) are increasingly adept at solving complex problems, such as mathematical reasoning and automatic evaluation.However, performance often degrades when prompts intertwine task instructions with rigid formatting requirements.This entanglement creates competing goals for the model, hindering its reasoning capabilities.To address this, we introduce DECO-G, a decoding framework that explicitly decouples format adherence from problem solving.DECO-G delegates format adherence to a separate Format Estimation Module (FEM), which performs probabilistic lookahead to estimate future format compliance rate and reweighs token probabilities, allowing the LLM to focus solely on task resolution.To make this approach both practical and efficient, we introduce three key innovations: instruction-aware distillation, a flexible trie-building algorithm, and HMM state pruning.Experiments across mathematical reasoning, event argument extraction, and LLM-asa-judge demonstrate that DECO-G constantly gains over prompting or structured generation baselines, with guaranteed format compliance.

While large language models have proven effective in a huge range of downstream applications, they often generate text that is problematic or lacks a desired attribute.In this paper, we introduce Reward-Augmented Decoding (RAD), a text generation procedure that uses a small unidirectional reward model to encourage a language model to generate text that has certain properties.Specifically, RAD uses the reward model to score generations as they are produced and rescales sampling probabilities to favor high-reward tokens.By using a unidirectional reward model, RAD can cache activations from prior generation steps to decrease computational overhead.Through experiments on generating non-toxic and sentimentcontrolled text, we demonstrate that RAD performs best among methods that change only the generation procedure and matches the performance of state-of-the-art methods that involve re-training the language model.We further validate that RAD is effective on very large language models while incurring a minimal computational overhead.

The Internet contains a wealth of knowledge—from the birthdays of historical figures to tutorials on how to code—all of which may be learned by language models. However, while certain pieces of information are ubiquitous on the web, others appear extremely rarely. In this paper, we study the relationship between the knowledge memorized by large language models and the information in pre-training datasets scraped from the web. In particular, we show that a language model’s ability to answer a fact-based question relates to how many documents associated with that question were seen during pre-training. We identify these relevant documents by entity linking pre-training datasets and counting documents that contain the same entities as a given question-answer pair. Our results demonstrate strong correlational and causal relationships between accuracy and relevant document count for numerous question answering datasets (e.g., TriviaQA), pre-training corpora (e.g., ROOTS), and model sizes (e.g., 176B parameters). Moreover, while larger models are better at learning long-tail knowledge, we estimate that today’s models must be scaled by many orders of magnitude to reach competitive QA performance on questions with little support in the pre-training data. Finally, we show that retrieval-augmentation can reduce the dependence on relevant pre-training information, presenting a promising approach for capturing the long-tail.