Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models
- URL: http://arxiv.org/abs/2405.00402v1
- Date: Wed, 1 May 2024 09:10:27 GMT
- Title: Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models
- Authors: Leonardo Ranaldi, Andrè Freitas,
- Abstract summary: The alignments of reasoning abilities between smaller and larger Language Models are largely conducted via Supervised Fine-Tuning (SFT)
We propose the Self-refine Instruction-tuning method that elicits Smaller Language Models to self-refine their abilities.
Results obtained on commonsense and math reasoning tasks show that this approach significantly outperforms Instruction-tuning in both in-domain and out-domain scenarios.
- Score: 0.8133739801185272
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: The alignments of reasoning abilities between smaller and larger Language Models are largely conducted via Supervised Fine-Tuning (SFT) using demonstrations generated from robust Large Language Models (LLMs). Although these approaches deliver more performant models, they do not show sufficiently strong generalization ability as the training only relies on the provided demonstrations. In this paper, we propose the Self-refine Instruction-tuning method that elicits Smaller Language Models to self-refine their abilities. Our approach is based on a two-stage process, where reasoning abilities are first transferred between LLMs and Small Language Models (SLMs) via Instruction-tuning on demonstrations provided by LLMs, and then the instructed models Self-refine their abilities through preference optimization strategies. In particular, the second phase operates refinement heuristics based on the Direct Preference Optimization algorithm, where the SLMs are elicited to deliver a series of reasoning paths by automatically sampling the generated responses and providing rewards using ground truths from the LLMs. Results obtained on commonsense and math reasoning tasks show that this approach significantly outperforms Instruction-tuning in both in-domain and out-domain scenarios, aligning the reasoning abilities of Smaller and Larger Language Models.
Related papers
- Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search [57.28671084993782]
Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains.
Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities.
We propose a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning.
arXiv Detail & Related papers (2025-02-04T17:26:58Z) - Scalable Language Models with Posterior Inference of Latent Thought Vectors [52.63299874322121]
Latent-Thought Language Models (LTMs) incorporate explicit latent thought vectors that follow an explicit prior model in latent space.
LTMs possess additional scaling dimensions beyond traditional LLMs, yielding a structured design space.
LTMs significantly outperform conventional autoregressive models and discrete diffusion models in validation perplexity and zero-shot language modeling.
arXiv Detail & Related papers (2025-02-03T17:50:34Z) - Large Language Models as Markov Chains [7.078696932669912]
We draw an equivalence between autoregressive transformer-based language models and Markov chains defined on a finite state space.
We relate the obtained results to the pathological behavior observed with LLMs.
Experiments with the most recent Llama and Gemma herds of models show that our theory correctly captures their behavior in practice.
arXiv Detail & Related papers (2024-10-03T17:45:31Z) - Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process [45.632012199451275]
In-context learning (ICL) is a few-shot learning paradigm that involves learning mappings through input-output pairs.
Existing works are highly dependent on large-scale labeled support sets, not always feasible in practical scenarios.
We introduce the Language Model-based Determinant Point Process (LM-DPP) that simultaneously considers the uncertainty and diversity of unlabeled instances for optimal selection.
arXiv Detail & Related papers (2024-08-04T18:08:15Z) - Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach [6.913791588789051]
We introduce a novel prompt-learning-based Large Language Model(LLM) framework that significantly improves prediction accuracy and provides explicit explanations for individual predictions.
We tested the framework's efficacy using two widely used choice datasets: London Passenger Mode Choice (LPMC) and Optima-Mode collected in Switzerland.
The results indicate that the LLM significantly outperforms state-of-the-art deep learning methods and discrete choice models in predicting people's choices.
arXiv Detail & Related papers (2024-06-19T13:46:08Z) - Characterizing Truthfulness in Large Language Model Generations with
Local Intrinsic Dimension [63.330262740414646]
We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs)
We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
arXiv Detail & Related papers (2024-02-28T04:56:21Z) - Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for
Large Language Models [125.91897197446379]
We find that MoE models benefit more from instruction tuning than dense models.
Our most powerful model, FLAN-MOE-32B, surpasses the performance of FLAN-PALM-62B on four benchmark tasks.
arXiv Detail & Related papers (2023-05-24T04:22:26Z) - Iterative Forward Tuning Boosts In-Context Learning in Language Models [88.25013390669845]
In this study, we introduce a novel two-stage framework to boost in-context learning in large language models (LLMs)
Specifically, our framework delineates the ICL process into two distinct stages: Deep-Thinking and test stages.
The Deep-Thinking stage incorporates a unique attention mechanism, i.e., iterative enhanced attention, which enables multiple rounds of information accumulation.
arXiv Detail & Related papers (2023-05-22T13:18:17Z) - Large Language Models Are Latent Variable Models: Explaining and Finding
Good Demonstrations for In-Context Learning [104.58874584354787]
In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning.
This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.
arXiv Detail & Related papers (2023-01-27T18:59:01Z) - Differentiable Prompt Makes Pre-trained Language Models Better Few-shot
Learners [23.150999852147283]
This study proposes a novel pluggable, and efficient approach named DifferentiAble pRompT (DART)
It can convert small language models into better few-shot learners without any prompt engineering.
A comprehensive evaluation of standard NLP tasks demonstrates that the proposed approach achieves a better few-shot performance.
arXiv Detail & Related papers (2021-08-30T12:29:25Z)
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.