Distributional reasoning in LLMs: Parallel reasoning processes in multi-hop reasoning
- URL: http://arxiv.org/abs/2406.13858v1
- Date: Wed, 19 Jun 2024 21:36:40 GMT
- Title: Distributional reasoning in LLMs: Parallel reasoning processes in multi-hop reasoning
- Authors: Yuval Shalev, Amir Feder, Ariel Goldstein,
- Abstract summary: We introduce a novel and interpretable analysis of internal multi-hop reasoning processes in large language models.
We show that during inference, the middle layers of the network generate highly interpretable embeddings.
Our findings can help uncover the strategies that LLMs use to solve reasoning tasks, offering insights into the types of thought processes that can emerge from artificial intelligence.
- Score: 8.609587510471943
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) have shown an impressive ability to perform tasks believed to require thought processes. When the model does not document an explicit thought process, it becomes difficult to understand the processes occurring within its hidden layers and to determine if these processes can be referred to as reasoning. We introduce a novel and interpretable analysis of internal multi-hop reasoning processes in LLMs. We demonstrate that the prediction process for compositional reasoning questions can be modeled using a simple linear transformation between two semantic category spaces. We show that during inference, the middle layers of the network generate highly interpretable embeddings that represent a set of potential intermediate answers for the multi-hop question. We use statistical analyses to show that a corresponding subset of tokens is activated in the model's output, implying the existence of parallel reasoning paths. These observations hold true even when the model lacks the necessary knowledge to solve the task. Our findings can help uncover the strategies that LLMs use to solve reasoning tasks, offering insights into the types of thought processes that can emerge from artificial intelligence. Finally, we also discuss the implication of cognitive modeling of these results.
Related papers
- LogiDynamics: Unraveling the Dynamics of Logical Inference in Large Language Model Reasoning [49.58786377307728]
This paper adopts an exploratory approach by introducing a controlled evaluation environment for analogical reasoning.
We analyze the comparative dynamics of inductive, abductive, and deductive inference pipelines.
We investigate advanced paradigms such as hypothesis selection, verification, and refinement, revealing their potential to scale up logical inference.
arXiv Detail & Related papers (2025-02-16T15:54:53Z) - Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning [52.83539473110143]
We introduce a novel structure-oriented analysis method to help Large Language Models (LLMs) better understand a question.
To further improve the reliability in complex question-answering tasks, we propose a multi-agent reasoning system, Structure-oriented Autonomous Reasoning Agents (SARA)
Extensive experiments verify the effectiveness of the proposed reasoning system. Surprisingly, in some cases, the system even surpasses few-shot methods.
arXiv Detail & Related papers (2024-10-18T05:30:33Z) - Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models [6.922021128239465]
Recent advances in AI have been driven by the capabilities of large language models (LLMs)
This paper introduces a framework that is both theoretical and practical, aimed at assessing how effectively LLMs are able to replicate real-world reasoning mechanisms.
arXiv Detail & Related papers (2024-08-15T15:19:11Z) - Reasoning with Large Language Models, a Survey [2.831296564800826]
This paper reviews the rapidly expanding field of prompt-based reasoning with LLMs.
Our taxonomy identifies different ways to generate, evaluate, and control multi-step reasoning.
We find that self-improvement, self-reflection, and some meta abilities of the reasoning processes are possible through the judicious use of prompts.
arXiv Detail & Related papers (2024-07-16T08:49:35Z) - LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning [61.7853049843921]
Chain-of-thought (CoT) prompting is a popular in-context learning approach for large language models (LLMs)
This paper introduces a new approach named Latent Reasoning Skills (LaRS) that employs unsupervised learning to create a latent space representation of rationales.
arXiv Detail & Related papers (2023-12-07T20:36:10Z) - 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) - Large Language Models are In-Context Semantic Reasoners rather than
Symbolic Reasoners [75.85554779782048]
Large Language Models (LLMs) have excited the natural language and machine learning community over recent years.
Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear.
In this work, we hypothesize that the learned textitsemantics of language tokens do the most heavy lifting during the reasoning process.
arXiv Detail & Related papers (2023-05-24T07:33:34Z) - 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.