$\texttt{LM}^\texttt{2}$: A Simple Society of Language Models Solves Complex Reasoning

• URL: http://arxiv.org/abs/2404.02255v1
• Date: Tue, 2 Apr 2024 19:23:10 GMT
• Title: $\texttt{LM}^\texttt{2}$: A Simple Society of Language Models Solves Complex Reasoning
• Authors: Gurusha Juneja, Subhabrata Dutta, Tanmoy Chakraborty,
• Abstract summary: Large Language Models (LLMS) often lose track of complex, multi-step reasoning. This paper proposes LM2 to address these challenges. LM2 modularizes the decomposition, solution, and verification into three different language models.
• Score: 22.810441504080703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite demonstrating emergent reasoning abilities, Large Language Models (LLMS) often lose track of complex, multi-step reasoning. Existing studies show that providing guidance via decomposing the original question into multiple subproblems elicits more robustness in LLM reasoning -- a decomposer generates the subproblems, and a solver solves each of these subproblems. However, these techniques fail to accommodate coordination between the decomposer and the solver modules (either in a single model or different specialized ones) -- the decomposer does not keep track of the ability of the solver to follow the decomposed reasoning. In this paper, we propose LM2 to address these challenges. LM2 modularizes the decomposition, solution, and verification into three different language models. The decomposer module identifies the key concepts necessary to solve the problem and generates step-by-step subquestions according to the reasoning requirement. The solver model generates the solution to the subproblems that are then checked by the verifier module; depending upon the feedback from the verifier, the reasoning context is constructed using the subproblems and the solutions. These models are trained to coordinate using policy learning. Exhaustive experimentation suggests the superiority of LM2 over existing methods on in- and out-domain reasoning problems, outperforming the best baselines by $8.1\%$ on MATH, $7.71\%$ on JEEBench, and $9.7\%$ on MedQA problems (code available at https://github.com/LCS2-IIITD/Language_Model_Multiplex).

Related papers

• FLARE: Faithful Logic-Aided Reasoning and Exploration [50.9814063216852]
  We introduce a novel approach for traversing the problem space using task decompositions. We use the Large Language Models to plan a solution, soft-formalise the query into facts and predicates using a logic programming code. Our method allows us to compute the faithfulness of the reasoning process w.r.t. the generated code and analyse the steps of the multi-hop search without relying on external solvers.
  arXiv  Detail & Related papers  (2024-10-14T19:39:11Z)
• Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs [2.3020018305241337]
  Distilling explicit chain-of-thought reasoning paths has emerged as an effective method for improving the reasoning abilities of large language models. We propose a novel approach to distill reasoning abilities from LLMs by leveraging their capacity to explain solutions. Our experiments demonstrate that learning from explanations enables the Reasoner to more effectively guide program implementation by a Coder.
  arXiv  Detail & Related papers  (2024-04-11T22:19:50Z)
• Divide-or-Conquer? Which Part Should You Distill Your LLM? [38.62667131299918]
  We devise a similar strategy that breaks down reasoning tasks into a problem decomposition phase and a problem solving phase. We show that the strategy is able to outperform a single stage solution.
  arXiv  Detail & Related papers  (2024-02-22T22:28:46Z)
• Look Before You Leap: A Universal Emergent Decomposition of Retrieval Tasks in Language Models [58.57279229066477]
  We study how language models (LMs) solve retrieval tasks in diverse situations. We introduce ORION, a collection of structured retrieval tasks spanning six domains. We find that LMs internally decompose retrieval tasks in a modular way.
  arXiv  Detail & Related papers  (2023-12-13T18:36:43Z)
• Language Models can be Logical Solvers [99.40649402395725]
  We introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers. LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers.
  arXiv  Detail & Related papers  (2023-11-10T16:23:50Z)
• Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning [41.03267013352519]
  Large Language Models (LLMs) prompted to generate chain-of-thought exhibit impressive reasoning capabilities. We introduce DaSLaM, which uses a decomposition generator to decompose complex problems into subproblems that require fewer reasoning steps. We show that DaSLaM is not limited by the solver's capabilities as a function of scale.
  arXiv  Detail & Related papers  (2023-10-21T15:23:20Z)
• CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules [51.82044734879657]
  We propose CodeChain, a novel framework for inference that elicits modularized code generation through a chain of self-revisions. We find that CodeChain can significantly boost both modularity as well as correctness of the generated solutions, achieving relative pass@1 improvements of 35% on APPS and 76% on CodeContests.
  arXiv  Detail & Related papers  (2023-10-13T10:17:48Z)
• Successive Prompting for Decomposing Complex Questions [50.00659445976735]
  Recent works leverage the capabilities of large language models (LMs) to perform complex question answering in a few-shot setting. We introduce Successive Prompting'', where we iteratively break down a complex task into a simple task, solve it, and then repeat the process until we get the final solution. Our best model (with successive prompting) achieves an improvement of 5% absolute F1 on a few-shot version of the DROP dataset.
  arXiv  Detail & Related papers  (2022-12-08T06:03:38Z)
• Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models [61.480085460269514]
  We propose a framework for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. We use this framework to build ModularQA, a system that can answer multi-hop reasoning questions by decomposing them into sub-questions answerable by a neural factoid single-span QA model and a symbolic calculator.
  arXiv  Detail & Related papers  (2020-09-01T23:45:42Z)

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.