Less for More: Enhancing Preference Learning in Generative Language Models with Automated Self-Curation of Training Corpora

• URL: http://arxiv.org/abs/2408.12799v1
• Date: Fri, 23 Aug 2024 02:27:14 GMT
• Title: Less for More: Enhancing Preference Learning in Generative Language Models with Automated Self-Curation of Training Corpora
• Authors: JoonHo Lee, JuYoun Son, Juree Seok, Wooseok Jang, Yeong-Dae Kwon,
• Abstract summary: Ambiguity in language presents challenges in developing more enhanced language models. We introduce a self-curation method that preprocesses annotated datasets by leveraging proxy models trained directly on these datasets. Our method enhances preference learning by automatically detecting and removing ambiguous annotations within the dataset.
• Score: 4.008122785948581
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ambiguity in language presents challenges in developing more enhanced language models, particularly in preference learning, where variability among annotators results in inconsistently annotated datasets used for model alignment. To address this issue, we introduce a self-curation method that preprocesses annotated datasets by leveraging proxy models trained directly on these datasets. Our method enhances preference learning by automatically detecting and removing ambiguous annotations within the dataset. The proposed approach is validated through extensive experiments, demonstrating a marked improvement in performance across various instruction-following tasks. Our work provides a straightforward and reliable method to overcome annotation inconsistencies, serving as an initial step towards the development of more advanced preference learning techniques.

Related papers

• Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models [0.0]
  We propose a method in which we use token-based and sentence-based augmentation methods to generate counterfactual sentence pairs. We show that the proposed method can improve the performance and robustness of the NLI model.
  arXiv  Detail & Related papers  (2024-10-28T03:43:25Z)
• Aligning Large Language Models via Self-Steering Optimization [78.42826116686435]
  We introduce Self-Steering Optimization ($SSO$), an algorithm that autonomously generates high-quality preference signals. $SSO$ maintains the accuracy of signals by ensuring a consistent gap between chosen and rejected responses. We validate the effectiveness of $SSO$ with two foundation models, Qwen2 and Llama3.1, indicating that it provides accurate, on-policy preference signals.
  arXiv  Detail & Related papers  (2024-10-22T16:04:03Z)
• How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics [49.9329723199239]
  We propose a method for the automated creation of a challenging test set without relying on the manual construction of artificial and unrealistic examples. We categorize the test set of popular NLI datasets into three difficulty levels by leveraging methods that exploit training dynamics. When our characterization method is applied to the training set, models trained with only a fraction of the data achieve comparable performance to those trained on the full dataset.
  arXiv  Detail & Related papers  (2024-10-04T13:39:21Z)
• Self-training Large Language Models through Knowledge Detection [26.831873737733737]
  Large language models (LLMs) often necessitate extensive labeled datasets and training compute to achieve impressive performance across downstream tasks. This paper explores a self-training paradigm, where the LLM autonomously curates its own labels and selectively trains on unknown data samples. Empirical evaluations demonstrate significant improvements in reducing hallucination in generation across multiple subjects.
  arXiv  Detail & Related papers  (2024-06-17T07:25:09Z)
• Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation [30.053409671898933]
  Kun is a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations. We leverage unlabelled data from diverse sources such as Wudao, Wanjuan, and SkyPile to generate a substantial dataset of over a million Chinese instructional data points.
  arXiv  Detail & Related papers  (2024-01-12T09:56:57Z)
• One-Shot Learning as Instruction Data Prospector for Large Language Models [108.81681547472138]
  textscNuggets uses one-shot learning to select high-quality instruction data from extensive datasets. We show that instruction tuning with the top 1% of examples curated by textscNuggets substantially outperforms conventional methods employing the entire dataset.
  arXiv  Detail & Related papers  (2023-12-16T03:33:12Z)
• ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference [16.73260713938154]
  A typical alignment procedure consists of supervised fine-tuning and preference learning. We introduce Point-wise Direct Preference Optimization, a novel preference learning method designed to harness point-wise feedback effectively. Our work also uncovers a novel connection between supervised fine-tuning and point-wise preference learning, culminating in Unified Language Model Alignment.
  arXiv  Detail & Related papers  (2023-12-05T07:52:12Z)
• Unsupervised 3D registration through optimization-guided cyclical self-training [71.75057371518093]
  State-of-the-art deep learning-based registration methods employ three different learning strategies. We propose a novel self-supervised learning paradigm for unsupervised registration, relying on self-training. We evaluate the method for abdomen and lung registration, consistently surpassing metric-based supervision and outperforming diverse state-of-the-art competitors.
  arXiv  Detail & Related papers  (2023-06-29T14:54:10Z)
• POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models [62.23255433487586]
  We propose an unsupervised fine-tuning framework to fine-tune the model or prompt on the unlabeled target data. We demonstrate how to apply our method to both language-augmented vision and masked-language models by aligning the discrete distributions extracted from the prompts and target data.
  arXiv  Detail & Related papers  (2023-04-29T22:05:22Z)
• SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization [21.037841262371355]
  We present a framework to transfer few-shot learning processes from source corpora to the target corpus. Our methods achieve state-of-the-art performance on six diverse corpora with 30.11%/33.95%/27.51% and 26.74%/31.14%/24.48% average improvements on ROUGE-1/2/L under 10- and 100-example settings.
  arXiv  Detail & Related papers  (2023-03-24T14:07:03Z)
• An Additive Instance-Wise Approach to Multi-class Model Interpretation [53.87578024052922]
  Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. Existing methods mainly focus on selecting explanatory input features, which follow either locally additive or instance-wise approaches. This work exploits the strengths of both methods and proposes a global framework for learning local explanations simultaneously for multiple target classes.
  arXiv  Detail & Related papers  (2022-07-07T06:50:27Z)
• Probing via Prompting [71.7904179689271]
  This paper introduces a novel model-free approach to probing, by formulating probing as a prompting task. We conduct experiments on five probing tasks and show that our approach is comparable or better at extracting information than diagnostic probes. We then examine the usefulness of a specific linguistic property for pre-training by removing the heads that are essential to that property and evaluating the resulting model's performance on language modeling.
  arXiv  Detail & Related papers  (2022-07-04T22:14:40Z)
• Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future [63.99570204416711]
  We reimplement 18 methods for detecting potential annotation errors and evaluate them on 9 English datasets. We define a uniform evaluation setup including a new formalization of the annotation error detection task. We release our datasets and implementations in an easy-to-use and open source software package.
  arXiv  Detail & Related papers  (2022-06-05T22:31:45Z)
• Prompt-Learning for Fine-Grained Entity Typing [40.983849729537795]
  We investigate the application of prompt-learning on fine-grained entity typing in fully supervised, few-shot and zero-shot scenarios. We propose a self-supervised strategy that carries out distribution-level optimization in prompt-learning to automatically summarize the information of entity types.
  arXiv  Detail & Related papers  (2021-08-24T09:39:35Z)
• Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learning [0.5729426778193398]
  We propose a deep reinforcement learning approach that transforms the optimization procedure into an end-to-end learnable process. Our experiments on real-world data show that our method is model-agnostic, relying only on feedback from model predictions.
  arXiv  Detail & Related papers  (2021-06-04T16:54:36Z)
• Online Coreset Selection for Rehearsal-based Continual Learning [65.85595842458882]
  In continual learning, we store a subset of training examples (coreset) to be replayed later to alleviate catastrophic forgetting. We propose Online Coreset Selection (OCS), a simple yet effective method that selects the most representative and informative coreset at each iteration. Our proposed method maximizes the model's adaptation to a target dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgetting.
  arXiv  Detail & Related papers  (2021-06-02T11:39:25Z)
• Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates [52.164757178369804]
  Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We conduct an empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pre-trained models in the active learning framework. We also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance.
  arXiv  Detail & Related papers  (2021-01-20T13:59:25Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)

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.