Dual Instruction Tuning with Large Language Models for Mathematical Reasoning

• URL: http://arxiv.org/abs/2403.18295v1
• Date: Wed, 27 Mar 2024 06:43:58 GMT
• Title: Dual Instruction Tuning with Large Language Models for Mathematical Reasoning
• Authors: Yongwei Zhou, Tiejun Zhao,
• Abstract summary: We propose a dual instruction tuning strategy to model mathematical reasoning from both forward and reverse directions. This involves introducing the Intermediate Reasoning State Prediction task (forward reasoning) and the Instruction Reconstruction task (reverse reasoning) to enhance the LLMs' understanding and execution of instructions. Comprehensive experiments validate the effectiveness and domain generalization of the dual instruction tuning strategy across various mathematical reasoning tasks.
• Score: 26.00472810721806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements highlight the success of instruction tuning with large language models (LLMs) utilizing Chain-of-Thought (CoT) data for mathematical reasoning tasks. Despite the fine-tuned LLMs, challenges persist, such as incorrect, missing, and redundant steps in CoT generation leading to inaccuracies in answer predictions. To alleviate this problem, we propose a dual instruction tuning strategy to meticulously model mathematical reasoning from both forward and reverse directions. This involves introducing the Intermediate Reasoning State Prediction task (forward reasoning) and the Instruction Reconstruction task (reverse reasoning) to enhance the LLMs' understanding and execution of instructions. Training instances for these tasks are constructed based on existing mathematical instruction tuning datasets. Subsequently, LLMs undergo multi-task fine-tuning using both existing mathematical instructions and the newly created data. Comprehensive experiments validate the effectiveness and domain generalization of the dual instruction tuning strategy across various mathematical reasoning tasks.

Related papers

• Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification [52.095460362197336]
  Large language models (LLMs) struggle with consistent and accurate reasoning. LLMs are trained primarily on correct solutions, reducing their ability to detect and learn from errors. We propose a novel collaborative method integrating Chain-of-Thought (CoT) and Program-of-Thought (PoT) solutions for verification.
  arXiv  Detail & Related papers  (2024-10-05T05:21:48Z)
• SwitchCIT: Switching for Continual Instruction Tuning of Large Language Models [14.085371250265224]
  Large language models (LLMs) have exhibited impressive capabilities in various domains, particularly in general language understanding. However these models, trained on massive text data, may not be finely optimized for specific tasks triggered by instructions. This work addresses the catastrophic forgetting in continual instruction learning for LLMs through a switching mechanism for routing computations to parameter-efficient tuned models.
  arXiv  Detail & Related papers  (2024-07-16T14:37:33Z)
• From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers [1.6958018695660049]
  We show that a more diverse instruction set, extending beyond code-related tasks, improves the performance of code generation. Our observations suggest that a more diverse semantic space for instruction-tuning sets greatly improves the model's ability to follow instructions and perform tasks.
  arXiv  Detail & Related papers  (2024-05-30T07:54:07Z)
• MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time [51.5039731721706]
  MindStar is a purely inference-based searching method for large language models. It formulates reasoning tasks as searching problems and proposes two search ideas to identify the optimal reasoning paths. It significantly enhances the reasoning abilities of open-source models, such as Llama-2-13B and Mistral-7B, and achieves comparable performance to GPT-3.5 and Grok-1.
  arXiv  Detail & Related papers  (2024-05-25T15:07:33Z)
• From Large to Tiny: Distilling and Refining Mathematical Expertise for Math Word Problems with Weakly Supervision [12.023661884821554]
  We introduce an innovative two-stage framework that adeptly transfers mathematical Expertise from large to tiny language models. Our method fully leverages the semantic understanding capabilities during the searching 'problem-equation' pair. It demonstrates significantly improved performance on the Math23K and Weak12K datasets compared to existing small model methods.
  arXiv  Detail & Related papers  (2024-03-21T13:29:54Z)
• CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model [121.23360004498893]
  We present a benchmark, namely Continual Instruction tuNing (CoIN), to assess existing MLLMs in the sequential instruction tuning paradigm. Experiments on CoIN demonstrate that current powerful MLLMs still suffer catastrophic forgetting. We introduce MoELoRA to MLLMs which is effective to retain the previous instruction alignment.
  arXiv  Detail & Related papers  (2024-03-13T08:54:31Z)
• Continual Instruction Tuning for Large Multimodal Models [30.438442723421556]
  Multi-task joint instruction tuning can facilitate the model's continual learning ability and forgetting. We propose task-similarity-informed regularization and model expansion methods for continual instruction tuning of LMMs.
  arXiv  Detail & Related papers  (2023-11-27T15:04:48Z)
• JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving [77.51817534090789]
  We propose textbfJiuZhang2.0, a unified Chinese PLM specially for multi-task mathematical problem solving. Our idea is to maintain a moderate-sized model and employ the emphcross-task knowledge sharing to improve the model capacity in a multi-task setting.
  arXiv  Detail & Related papers  (2023-06-19T15:45:36Z)
• Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning [75.74103236299477]
  Chain-of-thought prompting(CoT) and tool augmentation have been validated as effective practices for improving large language models. We propose a new approach that can deliberate the reasoning steps with tool interfaces, namely textbfDELI. Experimental results on CARP and six other datasets show that the proposed DELI mostly outperforms competitive baselines.
  arXiv  Detail & Related papers  (2023-06-04T17:02:59Z)
• SatLM: Satisfiability-Aided Language Models Using Declarative Prompting [68.40726892904286]
  We propose a new satisfiability-aided language modeling (SatLM) approach for improving the reasoning capabilities of large language models (LLMs) We use an LLM to generate a declarative task specification rather than an imperative program and leverage an off-the-shelf automated theorem prover to derive the final answer. We evaluate SATLM on 8 different datasets and show that it consistently outperforms program-aided LMs in the imperative paradigm.
  arXiv  Detail & Related papers  (2023-05-16T17:55:51Z)
• Cross-Task Consistency Learning Framework for Multi-Task Learning [9.991706230252708]
  We propose a new learning framework for 2-task MTL problem. We define two new loss terms inspired by cycle-consistency loss and contrastive learning. We theoretically prove that both losses help the model learn more efficiently and that cross-task consistency loss is better in terms of alignment with the straight-forward predictions.
  arXiv  Detail & Related papers  (2021-11-28T11:55:19Z)

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.