LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information

• URL: http://arxiv.org/abs/2502.02095v1
• Date: Tue, 04 Feb 2025 08:25:17 GMT
• Title: LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information
• Authors: Bowen Ping, Jiali Zeng, Fandong Meng, Shuo Wang, Jie Zhou, Shanghang Zhang,
• Abstract summary: Long-form generation is crucial for academic writing papers and repo-level code generation.<n>Existing methods that utilize preference learning with outcome supervision often fail to provide detailed feedback for extended contexts.<n>We propose enhancing long-form generation by incorporating process supervision.
• Score: 76.26257306813899
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-form generation is crucial for academic writing papers and repo-level code generation. Despite this, current models, including GPT-4o, still exhibit unsatisfactory performance. Existing methods that utilize preference learning with outcome supervision often fail to provide detailed feedback for extended contexts. This shortcoming can lead to content that does not fully satisfy query requirements, resulting in issues like length deviations, and diminished quality. In this paper, we propose enhancing long-form generation by incorporating process supervision. We employ Monte Carlo Tree Search to gather stepwise preference pairs, utilizing a global memory pool to maintain consistency. To address the issue of suboptimal candidate selection, we integrate external critiques to refine and improve the quality of the preference pairs. Finally, we apply step-level DPO using the collected stepwise preference pairs. Experimental results show that our method improves length and quality on long-form generation benchmarks, with almost lossless performance on general benchmarks across various model backbones.

Related papers

• From Token to Line: Enhancing Code Generation with a Long-Term Perspective [46.98293675904081]
  Large language models (LLMs) have significantly promoted the development of code generation task. We propose the textbfLSR-MCTS algorithm, which leverages MCTS to determine the code line-by-line and select the optimal path.
  arXiv  Detail & Related papers  (2025-04-10T04:03:25Z)
• Earlier Tokens Contribute More: Learning Direct Preference Optimization From Temporal Decay Perspective [22.248134630764497]
  We propose an enhanced preference optimization method that incorporates a temporal decay factor controlled by a gamma parameter. Our approach mitigates overfitting to less pertinent data and remains responsive to evolving human preferences.
  arXiv  Detail & Related papers  (2025-02-20T07:53:11Z)
• Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks [11.053340674721005]
  Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models by integrating external knowledge sources. This paper proposes an alternative paradigm, cache-augmented generation (CAG) that bypasses real-time retrieval.
  arXiv  Detail & Related papers  (2024-12-20T06:58:32Z)
• Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities [6.0211447492146]
  Large language models (LLMs) have shown remarkable performance across various tasks, yet their ability to handle long-context reading remains challenging. This study explores the effectiveness of leveraging high-quality academic peer review data for fine-tuning LLMs to enhance their long-context capabilities.
  arXiv  Detail & Related papers  (2024-11-07T22:57:02Z)
• What is Wrong with Perplexity for Long-context Language Modeling? [71.34933096461124]
  Long-context inputs are crucial for large language models (LLMs) in tasks such as extended conversations, document summarization, and many-shot in-context learning. Perplexity (PPL) has proven unreliable for assessing long-context capabilities. We propose bfLongPPL, a novel metric that focuses on key tokens by employing a long-short context contrastive method to identify them.
  arXiv  Detail & Related papers  (2024-10-31T09:39:28Z)
• GATEAU: Selecting Influential Sample for Long Context Alignment [62.87020831987625]
  GATEAU identifies influential samples enriched with long-range dependency relations.<n>Experiments indicate that GATEAU effectively identifies influential samples and the model trained on these selected samples exhibits better instruction-following and long-context understanding capabilities.
  arXiv  Detail & Related papers  (2024-10-21T04:30:53Z)
• HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly [34.205934899868346]
  We present HELMET, a comprehensive benchmark encompassing seven diverse, application-centric categories. We find that synthetic tasks like NIAH are not good predictors of downstream performance. While most LCLMs achieve perfect NIAH scores, open-source models significantly lag behind closed ones when the task requires full-context reasoning.
  arXiv  Detail & Related papers  (2024-10-03T17:20:11Z)
• Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA [71.04146366608904]
  Long-context modeling capabilities have garnered widespread attention, leading to the emergence of Large Language Models (LLMs) with ultra-context windows. We propose a novel long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) Loong introduces four types of tasks with a range of context lengths: Spotlight Locating, Comparison, Clustering, and Chain of Reasoning.
  arXiv  Detail & Related papers  (2024-06-25T09:42:56Z)
• Self-Exploring Language Models: Active Preference Elicitation for Online Alignment [88.56809269990625]
  We propose a bilevel objective optimistically biased towards potentially high-reward responses to actively explore out-of-distribution regions. Our experimental results demonstrate that when fine-tuned on Zephyr-7B-SFT and Llama-3-8B-Instruct models, Self-Exploring Language Models (SELM) significantly boosts the performance on instruction-following benchmarks.
  arXiv  Detail & Related papers  (2024-05-29T17:59:07Z)
• Long Context Alignment with Short Instructions and Synthesized Positions [56.1267385315404]
  This paper introduces Step-Skipping Alignment (SkipAlign) It is a new technique designed to enhance the long-context capabilities of Large Language Models (LLMs) With a careful selection of the base model and alignment datasets, SkipAlign with only 6B parameters achieves it's best performance and comparable with strong baselines like GPT-3.5-Turbo-16K on LongBench.
  arXiv  Detail & Related papers  (2024-05-07T01:56:22Z)
• Repoformer: Selective Retrieval for Repository-Level Code Completion [30.706277772743615]
  Recent advances in retrieval-augmented generation (RAG) have initiated a new era in repository-level code completion. In this paper, we propose a selective RAG framework to avoid retrieval when unnecessary. We show that our framework is able to accommodate different generation models, retrievers, and programming languages.
  arXiv  Detail & Related papers  (2024-03-15T06:59:43Z)
• Effective Long-Context Scaling of Foundation Models [90.57254298730923]
  We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our models achieve consistent improvements on most regular tasks and significant improvements on long-context tasks over Llama 2.
  arXiv  Detail & Related papers  (2023-09-27T21:41:49Z)

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.