OpenChat: Advancing Open-source Language Models with Mixed-Quality Data

• URL: http://arxiv.org/abs/2309.11235v2
• Date: Sat, 16 Mar 2024 04:32:25 GMT
• Title: OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
• Authors: Guan Wang, Sijie Cheng, Xianyuan Zhan, Xiangang Li, Sen Song, Yang Liu,
• Abstract summary: We present a novel framework, named OpenChat, to advance open-source language models with mixed-quality data. We propose the C(onditioned)-RLFT, which regards different data sources as coarse-grained reward labels and learns a class-conditioned policy. Our openchat-13b fine-tuned with C-RLFT achieves the highest average performance among all 13b open-source language models.
• Score: 29.938434364765534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, open-source large language models like LLaMA have emerged. Recent developments have incorporated supervised fine-tuning (SFT) and reinforcement learning fine-tuning (RLFT) to align these models with human goals. However, SFT methods treat all training data with mixed quality equally, while RLFT methods require high-quality pairwise or ranking-based preference data. In this study, we present a novel framework, named OpenChat, to advance open-source language models with mixed-quality data. Specifically, we consider the general SFT training data, consisting of a small amount of expert data mixed with a large proportion of sub-optimal data, without any preference labels. We propose the C(onditioned)-RLFT, which regards different data sources as coarse-grained reward labels and learns a class-conditioned policy to leverage complementary data quality information. Interestingly, the optimal policy in C-RLFT can be easily solved through single-stage, RL-free supervised learning, which is lightweight and avoids costly human preference labeling. Through extensive experiments on three standard benchmarks, our openchat-13b fine-tuned with C-RLFT achieves the highest average performance among all 13b open-source language models. Moreover, we use AGIEval to validate the model generalization performance, in which only openchat-13b surpasses the base model. Finally, we conduct a series of analyses to shed light on the effectiveness and robustness of OpenChat. Our code, data, and models are publicly available at https://github.com/imoneoi/openchat and https://huggingface.co/openchat.

Related papers

• Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved) [5.000768714035796]
  We draw on a connection between supervised fine-tuning (SFT) and the theory and practice of finding optimal policies via Reinforcement Learning (RL)<n>We show that a small modification to SFT leads to an importance weighted variant that behaves closer to training with RL as it.<n>We refer to this variant as importance weighted supervised fine-tuning (iw-SFT)
  arXiv  Detail & Related papers  (2025-07-17T07:26:54Z)
• Approximating Language Model Training Data from Weights [70.08614275061689]
  We formalize the problem of data approximation from model weights and propose several baselines and metrics.<n>We develop a gradient-based approach that selects the highest-matching data from a large public text corpus.<n>Even when none of the true training data is known, our method is able to locate a small subset of public Web documents.
  arXiv  Detail & Related papers  (2025-06-18T15:26:43Z)
• The Best Instruction-Tuning Data are Those That Fit [17.401088816596054]
  Supervised fine-tuning (SFT) data are crucial for eliciting strong capabilities from pretrained large language models (LLMs) We propose **GRAPE**, a novel SFT framework that accounts for the unique characteristics of the target model. For each instruction, it gathers responses from various LLMs and selects the one with the highest probability measured by the target model.
  arXiv  Detail & Related papers  (2025-02-06T16:31:21Z)
• Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models [146.18107944503436]
  Molmo is a new family of VLMs that are state-of-the-art in their class of openness. Our key innovation is a novel, highly detailed image caption dataset collected entirely from human annotators. We will be releasing all of our model weights, captioning and fine-tuning data, and source code in the near future.
  arXiv  Detail & Related papers  (2024-09-25T17:59:51Z)
• Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review [50.78587571704713]
  Learn-Focus-Review (LFR) is a dynamic training approach that adapts to the model's learning progress. LFR tracks the model's learning performance across data blocks (sequences of tokens) and prioritizes revisiting challenging regions of the dataset. Compared to baseline models trained on the full datasets, LFR consistently achieved lower perplexity and higher accuracy.
  arXiv  Detail & Related papers  (2024-09-10T00:59:18Z)
• $\textbf{PLUM}$: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases [28.791570350483816]
  We propose PLUM, an on-policy $textbfP$reference $textbfL$earning framework A$textbfu$gmented with test cases for code L$textbfM$ s. PLUM levitates the need to train reward models, allowing for large scale on-policy and online preference data collation. We show PLUM's benefits are consistent across various widely-used code LMs even they have been well-trained with SFT.
  arXiv  Detail & Related papers  (2024-06-11T02:07:18Z)
• Aligning Large Language Models with Self-generated Preference Data [72.99676237703099]
  We propose a new framework that boosts the alignment of large language models (LLMs) with human preferences. Our key idea is leveraging the human prior knowledge within the small (seed) data. We introduce a noise-aware preference learning algorithm to mitigate the risk of low quality within generated preference data.
  arXiv  Detail & Related papers  (2024-06-06T18:01:02Z)
• CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning [19.100022935748225]
  Data selection has emerged as a core issue for large-scale visual-language model pretaining (e.g., CLIP) Three main data selection approaches are: (1) leveraging external non-CLIP models to aid data selection, (2) training new CLIP-style embedding models that are more effective at selecting high-quality data, and (3) designing better metrics or strategies universally applicable to any CLIP embedding.
  arXiv  Detail & Related papers  (2024-05-29T22:19:57Z)
• Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment [65.15914284008973]
  We propose to leverage an Inverse Reinforcement Learning (IRL) technique to simultaneously build an reward model and a policy model. We show that the proposed algorithms converge to the stationary solutions of the IRL problem. Our results indicate that it is beneficial to leverage reward learning throughout the entire alignment process.
  arXiv  Detail & Related papers  (2024-05-28T07:11:05Z)
• RLHF Workflow: From Reward Modeling to Online RLHF [79.83927049253924]
  We present the workflow of Online Iterative Reinforcement Learning from Human Feedback (RLHF) in this technical report. RLHF is widely reported to outperform its offline counterpart by a large margin in the recent large language model (LLM) literature. We show that supervised fine-tuning (SFT) and iterative RLHF can obtain state-of-the-art performance with fully open-source datasets.
  arXiv  Detail & Related papers  (2024-05-13T15:50:39Z)
• Fine-Tuning Language Models with Reward Learning on Policy [68.70065254564642]
  Reinforcement learning from human feedback (RLHF) has emerged as an effective approach to aligning large language models (LLMs) to human preferences. Despite its popularity, (fixed) reward models may suffer from inaccurate off-distribution. We propose reward learning on policy (RLP), an unsupervised framework that refines a reward model using policy samples to keep it on-distribution.
  arXiv  Detail & Related papers  (2024-03-28T10:02:10Z)
• Exploiting Label Skews in Federated Learning with Model Concatenation [39.38427550571378]
  Federated Learning (FL) has emerged as a promising solution to perform deep learning on different data owners without exchanging raw data. Among different non-IID types, label skews have been challenging and common in image classification and other tasks. We propose FedConcat, a simple and effective approach that degrades these local models as the base of the global model.
  arXiv  Detail & Related papers  (2023-12-11T10:44:52Z)
• Enhancing Chat Language Models by Scaling High-quality Instructional Conversations [91.98516412612739]
  We first provide a systematically designed, diverse, informative, large-scale dataset of instructional conversations, UltraChat. Our objective is to capture the breadth of interactions that a human might have with an AI assistant. We fine-tune a LLaMA model to create a powerful conversational model, UltraLLaMA.
  arXiv  Detail & Related papers  (2023-05-23T16:49:14Z)
• Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling [96.8742582581744]
  We present an open-world unlabeled data sampling framework called Model-Aware K-center (MAK) MAK follows three simple principles: tailness, proximity, and diversity. We demonstrate that MAK can consistently improve both the overall representation quality and the class balancedness of the learned features.
  arXiv  Detail & Related papers  (2021-11-01T15:09:41Z)
• Distilled One-Shot Federated Learning [13.294757670979031]
  We propose Distilled One-Shot Federated Learning (DOSFL) to significantly reduce the communication cost while achieving comparable performance. In just one round, each client distills their private dataset, sends the synthetic data (e.g. images or sentences) to the server, and collectively trains a global model. With this weight-less and gradient-less design, the total communication cost of DOSFL is up to three orders of magnitude less than FedAvg.
  arXiv  Detail & Related papers  (2020-09-17T01:14:47Z)

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.