Cascaded Self-Evaluation Augmented Training for Lightweight Multimodal LLMs

• URL: http://arxiv.org/abs/2501.05662v2
• Date: Sun, 16 Mar 2025 02:28:32 GMT
• Title: Cascaded Self-Evaluation Augmented Training for Lightweight Multimodal LLMs
• Authors: Zheqi Lv, Wenkai Wang, Jiawei Wang, Shengyu Zhang, Fei Wu,
• Abstract summary: Multimodal Large Language Models (EMLLMs) can improve performance through Chain-of-Thought (CoT) reasoning.<n>They have poor self-evaluation capabilities during the CoT reasoning process.<n>This is due to their tendency to simplify the reasoning process and the degradation of self-evaluation ability during downstream task fine-tuning.
• Score: 14.763433457556136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient Multimodal Large Language Models (EMLLMs) can improve performance through Chain-of-Thought (CoT) reasoning, but they have poor self-evaluation capabilities during the CoT reasoning process. This is due to their tendency to simplify the reasoning process and the degradation of self-evaluation ability during downstream task fine-tuning. To address this, we intuitively propose \textit{Self-Evaluation Augmented Training (SEAT)}, which uses more powerful EMLLMs to evaluate CoT reasoning data. The evaluation data is then used to train EMLLMs. However, due to the difficulties EMLLMs face with processing long token input-output sequences, and the degradation of self-evaluation ability as a basis for CoT reasoning, the SEAT method is not fully adapted. Therefore, we further propose \textit{Cascaded Self-Evaluation Augmented Training (Cas-SEAT)}, which converts long prompts into cascaded short prompts, each focusing on a specific task. Additionally, we mix CoT reasoning and self-evaluation data to preserve its CoT reasoning ability while enhancing the self-evaluation capability of EMLLMs. We also conduct \textit{Double-level Data Filtering (DDF)}, which includes source data filtering and labeled data filtering, using both manual selection and MLLMs for filtering. Cas-SEAT and DDF work together to improve the performance of EMLLMs. Experiments show that Cas-SEAT achieves an average improvement of 22.16% across multiple datasets, and DDF significantly reduces the resource consumption of training

Related papers

• InfiAlign: A Scalable and Sample-Efficient Framework for Aligning LLMs to Enhance Reasoning Capabilities [27.09178257629886]
  InfiAlign is a scalable and sample-efficient post-training framework for large language models (LLMs)<n>At the core of InfiAlign is a robust data selection pipeline that automatically curates high-quality alignment data from open-source reasoning.<n>Our results highlight the effectiveness of combining principled data selection with full-stage post-training.
  arXiv  Detail & Related papers  (2025-08-07T15:34:06Z)
• SPaRFT: Self-Paced Reinforcement Fine-Tuning for Large Language Models [51.74498855100541]
  Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL)<n>We propose textbfSPaRFT, a self-paced learning framework that enables efficient learning based on the capability of the model being trained.
  arXiv  Detail & Related papers  (2025-08-07T03:50:48Z)
• TACO: Think-Answer Consistency for Optimized Long-Chain Reasoning and Efficient Data Learning via Reinforcement Learning in LVLMs [50.820065021136024]
  DeepSeek R1 has significantly advanced complex reasoning for large language models (LLMs)<n>Recent methods have attempted to replicate R1's reasoning capabilities in multimodal settings.<n>We propose TACO, a novel reinforcement learning algorithm for visual reasoning.
  arXiv  Detail & Related papers  (2025-05-27T06:30:48Z)
• TRACT: Regression-Aware Fine-tuning Meets Chain-of-Thought Reasoning for LLM-as-a-Judge [59.57934574562651]
  TRACT (Two-stage Regression-Aware fine-tuning with CoT) is a method combining CoT reasoning with regression-aware training. Experiments across four LLM-as-a-judge datasets and two LLMs show that TRACT significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2025-03-06T12:33:20Z)
• The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models [69.798277882245]
  We introduce Unsupervised Prefix Fine-Tuning (UPFT) to enhance large language models' reasoning efficiency. UPFT removes the need for labeled data or exhaustive sampling. Experiments show that UPFT matches the performance of supervised methods.
  arXiv  Detail & Related papers  (2025-03-04T18:56:03Z)
• Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models [39.22557129190619]
  Distillation--post-training on LRMs-generated data--is a straightforward yet effective method to enhance the reasoning abilities of smaller models.<n>To alleviate this bottleneck, we propose constructing tree-based CoT data from scratch via Monte Carlo Tree Search.<n>We then exploit a set of CoT-aware approaches, including Thoughts Length Balance, Fine-grained DPO, and Joint Post-training Objective, to enhance SFT and RL on the constructed data.
  arXiv  Detail & Related papers  (2025-03-03T12:17:36Z)
• RankCoT: Refining Knowledge for Retrieval-Augmented Generation through Ranking Chain-of-Thoughts [23.383151362974488]
  RankCoT is a knowledge refinement method that incorporates reranking signals in generating CoT-based summarization. Our experiments demonstrate the effectiveness of RankCoT, showing its superior performance over other knowledge refinement models.
  arXiv  Detail & Related papers  (2025-02-25T06:18:05Z)
• S$^2$R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning [51.84977135926156]
  We introduce S$2$R, an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference. Our results demonstrate that Qwen2.5-math-7B achieves an accuracy improvement from 51.0% to 81.6%, outperforming models trained on an equivalent amount of long-CoT distilled data.
  arXiv  Detail & Related papers  (2025-02-18T13:40:22Z)
• DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks [40.91931801667421]
  This paper presents a novel global-to-local algorithm called DUET that can exploit the feedback loop by interleaving a data selection method with Bayesian optimization. As a result, DUET can efficiently refine the training data mixture from a pool of data domains to maximize the model's performance on the unseen evaluation task.
  arXiv  Detail & Related papers  (2025-02-01T01:52:32Z)
• MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale [66.73529246309033]
  multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks.<n>Existing instruction-tuning datasets only provide phrase-level answers without any intermediate rationales.<n>We introduce a scalable and cost-effective method to construct a large-scale multimodal instruction-tuning dataset with rich intermediate rationales.
  arXiv  Detail & Related papers  (2024-12-06T18:14:24Z)
• Understanding Chain-of-Thought in LLMs through Information Theory [16.78730663293352]
  We formalize Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) through an information-theoretic lens. Specifically, our framework quantifies the information gain' at each reasoning step, enabling the identification of failure modes. We demonstrate the efficacy of our approach through extensive experiments on toy and GSM-8K data, where it significantly outperforms existing outcome-based methods.
  arXiv  Detail & Related papers  (2024-11-18T19:14:36Z)
• Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks [20.072783454089098]
  This paper presents AutoEval, a novel benchmark for scaling Large Language Model (LLM) assessment in formal tasks with clear notions of correctness. AutoEval is the first benchmarking paradigm that offers several key advantages necessary for scaling objective evaluation of LLMs without human labeling.
  arXiv  Detail & Related papers  (2024-10-11T00:56:37Z)
• SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models [54.78329741186446]
  We propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. Experiments across both in-domain and out-of-domain benchmarks in English and Chinese demonstrate the effectiveness of the proposed paradigm.
  arXiv  Detail & Related papers  (2024-08-28T06:33:03Z)
• On Speeding Up Language Model Evaluation [48.51924035873411]
  Development of prompt-based methods with Large Language Models (LLMs) requires making numerous decisions. We propose a novel method to address this challenge. We show that it can identify the top-performing method using only 5-15% of the typically needed resources.
  arXiv  Detail & Related papers  (2024-07-08T17:48:42Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extraction [13.245873138716044]
  Emotion-Cause Pair Extraction (ECPE) involves extracting clause pairs representing emotions and their causes in a document. Inspired by recent work, we explore leveraging large language model (LLM) to address ECPE task without additional training. We introduce chain-of-thought to mimic human cognitive process and propose the Decomposed Emotion-Cause Chain (DECC) framework.
  arXiv  Detail & Related papers  (2024-01-31T10:20:01Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding [38.11411155621616]
  We study self-training as one of the predominant semi-supervised learning approaches. We present UPET, a novel Uncertainty-aware self-Training framework. We show that UPET achieves a substantial improvement in terms of performance and efficiency.
  arXiv  Detail & Related papers  (2023-10-19T02:18:29Z)
• From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
  We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
  arXiv  Detail & Related papers  (2023-08-23T09:45:29Z)

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.