Nudging: Inference-time Alignment of LLMs via Guided Decoding

• URL: http://arxiv.org/abs/2410.09300v3
• Date: Sun, 20 Apr 2025 00:16:18 GMT
• Title: Nudging: Inference-time Alignment of LLMs via Guided Decoding
• Authors: Yu Fei, Yasaman Razeghi, Sameer Singh,
• Abstract summary: We propose nudging, a plug-and-play algorithm that aligns any base model at inference time using a small aligned model.<n>Nudging is motivated by recent findings that alignment primarily alters the model's behavior on a small subset of stylistic tokens.<n>We evaluate nudging across 3 model families on a diverse range of open-instruction tasks.
• Score: 18.530367090350605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) require alignment to effectively and safely follow user instructions. This process necessitates training an aligned version for every base model, resulting in significant computational overhead. In this work, we propose nudging, a simple, plug-and-play, and training-free algorithm that aligns any base model at inference time using a small aligned model. Nudging is motivated by recent findings that alignment primarily alters the model's behavior on a small subset of stylistic tokens (e.g., discourse markers). We find that base models are significantly more uncertain when generating these tokens. Building on this insight, nudging employs a small aligned model to generate nudging tokens to guide the base model's output during decoding when the base model's uncertainty is high. We evaluate nudging across 3 model families on a diverse range of open-instruction tasks. Without any training, nudging a large base model with a 7x-14x smaller aligned model achieves zero-shot performance comparable to, and sometimes surpassing, that of large aligned models. By operating at the token level, nudging enables off-the-shelf collaboration between model families. For instance, nudging Gemma-2-27b with Llama-2-7b-chat outperforms Llama-2-70b-chat on various tasks. Overall, our work offers a modular and cost-efficient solution to LLM alignment. Our project website: https://fywalter.github.io/nudging/ .

Related papers

• STAR: Spectral Truncation and Rescale for Model Merging [48.19545750399348]
  A key challenge in model merging is the seemingly inevitable decrease in task performance as the number of models increases. We propose $mathbfS$pectral $mathbfT$runcation $mathbfA$nd $mathbfR$escale (STAR) that aims at mitigating merging conflicts'' We demonstrate the effectiveness of STAR through extensive model merging cases on diverse NLP tasks.
  arXiv  Detail & Related papers  (2025-02-14T17:59:58Z)
• Cross-model Control: Improving Multiple Large Language Models in One-time Training [34.98931804630706]
  Cross-model Control (CMC) is a method that improves multiple large language models in one-time training. Based on this insight, we incorporate a tiny language model with a minimal number of parameters. We propose a novel token mapping strategy named PM-MinED to make this tiny language model applicable to models with different vocabularies.
  arXiv  Detail & Related papers  (2024-10-23T06:52:09Z)
• What Matters for Model Merging at Scale? [94.26607564817786]
  Model merging aims to combine multiple expert models into a more capable single model. Previous studies have primarily focused on merging a few small models. This study systematically evaluates the utility of model merging at scale.
  arXiv  Detail & Related papers  (2024-10-04T17:17:19Z)
• Enabling Small Models for Zero-Shot Classification through Model Label Learning [50.68074833512999]
  We introduce a novel paradigm, Model Label Learning (MLL), which bridges the gap between models and their functionalities. Experiments on seven real-world datasets validate the effectiveness and efficiency of MLL.
  arXiv  Detail & Related papers  (2024-08-21T09:08:26Z)
• Large Language Model Pruning [0.0]
  We suggest a model pruning technique specifically focused on LLMs. The proposed methodology emphasizes the explainability of deep learning models. We also explore the difference between pruning on large-scale models vs. pruning on small-scale models.
  arXiv  Detail & Related papers  (2024-05-24T18:22:15Z)
• EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
  We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
  arXiv  Detail & Related papers  (2024-05-23T05:25:45Z)
• First-Step Advantage: Importance of Starting Right in Multi-Step Math Reasoning [11.75364271481855]
  Language models can solve complex reasoning tasks better by learning to generate rationales for their predictions. We observe that smaller models in particular when corrected, can solve a task that they would have otherwise struggled with. We propose QuestCoT, where a smaller model first asks itself how to start, before proceeding with a chain of reasoning.
  arXiv  Detail & Related papers  (2023-11-14T06:45:31Z)
• Zephyr: Direct Distillation of LM Alignment [59.03530095974505]
  We aim to produce a smaller language model that is aligned to user intent. Previous research has shown that applying supervised fine-tuning (dSFT) on larger models significantly improves task accuracy. We apply distilled direct preference optimization (dDPO) to learn a chat model with significantly improved intent alignment.
  arXiv  Detail & Related papers  (2023-10-25T19:25:16Z)
• Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning [52.29522018586365]
  We study structured pruning as an effective means to develop smaller LLMs from pre-trained, larger models. Our approach employs two key techniques: (1) targeted structured pruning, which prunes a larger model to a specified target shape by removing layers, heads, and intermediate and hidden dimensions in an end-to-end manner, and (2) dynamic batch loading, which dynamically updates the composition of sampled data in each training batch based on varying losses across different domains.
  arXiv  Detail & Related papers  (2023-10-10T15:13:30Z)
• "Medium" LMs of Code in the Era of LLMs: Lessons From StackOverflow [5.036273913335737]
  We train two models: SOBertBase, with 109M parameters, and SOBertLarge with 762M parameters, at a budget of just $$187$ and $$800$ each. Results demonstrate that pre-training both extensively and properly on in-domain data can yield a powerful and affordable alternative to leveraging closed-source general-purpose models.
  arXiv  Detail & Related papers  (2023-06-05T21:38:30Z)
• eP-ALM: Efficient Perceptual Augmentation of Language Models [70.47962271121389]
  We propose to direct effort to efficient adaptations of existing models, and propose to augment Language Models with perception. Existing approaches for adapting pretrained models for vision-language tasks still rely on several key components that hinder their efficiency. We show that by freezing more than 99% of total parameters, training only one linear projection layer, and prepending only one trainable token, our approach (dubbed eP-ALM) significantly outperforms other baselines on VQA and Captioning.
  arXiv  Detail & Related papers  (2023-03-20T19:20:34Z)
• Deep Model Assembling [31.88606253639418]
  This paper studies a divide-and-conquer strategy to train large models. It divides a large model into smaller modules, training them independently, and reassembling the trained modules to obtain the target model. We introduce a global, shared meta model to implicitly link all the modules together. This enables us to train highly compatible modules that collaborate effectively when they are assembled together.
  arXiv  Detail & Related papers  (2022-12-08T08:04:06Z)
• On the Compositional Generalization Gap of In-Context Learning [73.09193595292233]
  We look at the gap between the in-distribution (ID) and out-of-distribution (OOD) performance of such models in semantic parsing tasks with in-context learning. We evaluate four model families, OPT, BLOOM, CodeGen and Codex on three semantic parsing datasets.
  arXiv  Detail & Related papers  (2022-11-15T19:56:37Z)
• CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing [83.63107444454938]
  We propose a consistency-regularized ensemble learning approach based on perturbed models, named CAMERO. Specifically, we share the weights of bottom layers across all models and apply different perturbations to the hidden representations for different models, which can effectively promote the model diversity. Our experiments using large language models demonstrate that CAMERO significantly improves the generalization performance of the ensemble model.
  arXiv  Detail & Related papers  (2022-04-13T19:54:51Z)
• One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective [86.48094395282546]
  A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. We propose a novel deep hashing model with only a single learning objective. Our model is highly effective, outperforming the state-of-the-art multi-loss hashing models on three large-scale instance retrieval benchmarks.
  arXiv  Detail & Related papers  (2021-09-29T14:27:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.