First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning
- URL: http://arxiv.org/abs/2406.16078v2
- Date: Mon, 07 Oct 2024 15:01:26 GMT
- Title: First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning
- Authors: Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi, Shusaku Sone, Masaya Taniguchi, Keisuke Sakaguchi, Kentaro Inui,
- Abstract summary: Multi-step reasoning instruction is widely adopted to explore better language performance.
We report on the systematic strategy that LMs employ in such a multi-step reasoning process.
- Score: 26.732781911221636
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multi-step reasoning instruction, such as chain-of-thought prompting, is widely adopted to explore better language models (LMs) performance. We report on the systematic strategy that LMs employ in such a multi-step reasoning process. Our controlled experiments reveal that LMs rely more heavily on heuristics, such as lexical overlap, in the earlier stages of reasoning, where more reasoning steps remain to reach a goal. Conversely, their reliance on heuristics decreases as LMs progress closer to the final answer through multiple reasoning steps. This suggests that LMs can backtrack only a limited number of future steps and dynamically combine heuristic strategies with rationale ones in tasks involving multi-step reasoning.
Related papers
- R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization [86.32257216965229]
We propose a new online reinforcement learning framework that enables MLLMs to self-improve reasoning ability via simple, effective and dense step-wise rewarding.
StepGRPO introduces two novel rule-based reasoning rewards: Step-wise Reasoning Accuracy Reward (StepRAR) and Step-wise Reasoning Validity Reward (StepRVR)
With the proposed StepGRPO, we introduce R1-VL, a series of MLLMs with outstanding capabilities in step-by-step reasoning.
arXiv Detail & Related papers (2025-03-17T08:51:44Z) - Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models [31.556646366268286]
Large Language Models increasingly rely on prolonged reasoning chains to solve complex tasks.
This trial-and-error approach often leads to high computational overhead and error propagation.
We introduce Meta-Reasoner, a framework that dynamically optimize inference-time reasoning.
arXiv Detail & Related papers (2025-02-27T09:40:13Z) - Stepwise Informativeness Search for Efficient and Effective LLM Reasoning [49.85349030928302]
Recent studies show that Large Language Models (LLMs) tend to lose focus over the middle of long contexts.
We propose guiding LLMs to generate more accurate and concise step-by-step rationales.
arXiv Detail & Related papers (2025-02-21T09:39:27Z) - A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics [9.681821524089761]
We present a survey of strategies utilizing feedback at the step and outcome levels to enhance multi-step math reasoning for LLMs.
As multi-step reasoning emerges a crucial component in scaling LLMs, we hope to establish its foundation for easier understanding and empower further research.
arXiv Detail & Related papers (2025-02-20T07:31:00Z) - Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks.
LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning.
We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
arXiv Detail & Related papers (2024-06-20T13:08:09Z) - Look Before You Decide: Prompting Active Deduction of MLLMs for Assumptive Reasoning [68.83624133567213]
We show that most prevalent MLLMs can be easily fooled by the introduction of a presupposition into the question.
We also propose a simple yet effective method, Active Deduction (AD), to encourage the model to actively perform composite deduction.
arXiv Detail & Related papers (2024-04-19T15:53:27Z) - Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof
Generation with Contrastive Stepwise Decoding [11.385103498440932]
We introduce contrastive decoding to stepwise proof generation, making use of negative reasoning paths to strengthen the model's capacity for logical deduction.
Experiments on EntailmentBank underscore the success of our method in augmenting the proof planning abilities of language models.
arXiv Detail & Related papers (2023-11-12T05:12:49Z) - DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy [76.58614128865652]
We propose DetermLR, a novel perspective that rethinks the reasoning process as an evolution from indeterminacy to determinacy.
First, we categorize known conditions into two types: determinate and indeterminate premises This provides an oveall direction for the reasoning process and guides LLMs in converting indeterminate data into progressively determinate insights.
We automate the storage and extraction of available premises and reasoning paths with reasoning memory, preserving historical reasoning details for subsequent reasoning steps.
arXiv Detail & Related papers (2023-10-28T10:05:51Z) - Towards a Mechanistic Interpretation of Multi-Step Reasoning
Capabilities of Language Models [107.07851578154242]
Language models (LMs) have strong multi-step (i.e., procedural) reasoning capabilities.
It is unclear whether LMs perform tasks by cheating with answers memorized from pretraining corpus, or, via a multi-step reasoning mechanism.
We show that MechanisticProbe is able to detect the information of the reasoning tree from the model's attentions for most examples.
arXiv Detail & Related papers (2023-10-23T01:47:29Z) - Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models [56.34029644009297]
Large language models (LLMs) have demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems.
LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning.
We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model.
arXiv Detail & Related papers (2023-10-02T01:00:50Z) - Reason for Future, Act for Now: A Principled Framework for Autonomous
LLM Agents with Provable Sample Efficiency [53.8779374188643]
We propose a principled framework with provable regret guarantees to orchestrate reasoning and acting.
Specifically, we design a prompt template for reasoning that learns from the memory buffer and plans a future trajectory over a long horizon.
At each step, the LLM agent takes the initial action of the planned trajectory ("act for now"), stores the collected feedback in the memory buffer, and reinvokes the reasoning routine to replan the future trajectory from the new state.
arXiv Detail & Related papers (2023-09-29T16:36:39Z) - Faithful Reasoning Using Large Language Models [12.132449274592668]
We show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem.
Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned LMs.
We demonstrate the effectiveness of our model on multi-step logical deduction and scientific question-answering, showing that it outperforms baselines on final answer accuracy.
arXiv Detail & Related papers (2022-08-30T13:44:41Z)
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.