MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the challenges of structured reasoning tasks, specifically focusing on generating structured explanations that connect arguments to beliefs, inferring dependencies among events, and handling style discrepancies and error propagation in structured response generation . The paper introduces a novel approach called MIDGARD, which leverages the self-consistency strategy to sample diverse reasoning paths and construct an aggregate graph to alleviate error propagation in structured reasoning tasks . This problem is not entirely new, but the paper proposes a unique methodology to enhance performance in structured commonsense reasoning tasks by aggregating information from diverse samples generated by Large Language Models (LLMs) .
What scientific hypothesis does this paper seek to validate?
The scientific hypothesis that the paper "MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning" seeks to validate is related to structured reasoning tasks using large language models (LLMs) to generate reasoning graphs from natural language input. The hypothesis revolves around leveraging Minimum Description Length (MDL)-based formulation to identify consistent properties among different graph samples generated by an LLM. This formulation aims to reject properties that appear in only a few samples, which are likely to be erroneous, while including missing elements without compromising precision .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper "MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning" proposes several new ideas, methods, and models in the field of structured commonsense reasoning . Here are some key points from the paper:
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Self-Consistency Framework: The paper introduces a self-consistency framework based on the Minimum Description Length (MDL) principle. This framework aims to efficiently represent datasets using the fewest bits while minimizing model complexity .
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Graph Construction: The paper describes a method for constructing optimal aggregate graphs by formulating an objective to determine the optimal values for nodes and edges in the graph .
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Hyperparameter Selection: The paper discusses the automatic selection of hyperparameters {λ1, λ2} using k-fold cross-validation with few-shot examples .
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Model Evaluation: The paper evaluates the proposed approach using base LLMs such as gpt-3.5-turbo2 and CODE-LLAMA, comparing it with a GREEDY baseline and other variants of the MIDGARD model .
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Precision/Recall Analysis: The paper conducts a precision/recall analysis for argument structure extraction to demonstrate the effectiveness of the algorithm in reducing errors and capturing genuine properties from multiple samples .
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Related Works Comparison: The paper compares its approach with existing sampling-based approaches using Large Language Models (LLMs) for NLP and commonsense reasoning tasks, highlighting the limitations of post-hoc strategies and training-based sampling .
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Task-Specific Applications: The paper applies the proposed framework to tasks such as mathematical proof generation, language modeling, and argument structure extraction, showcasing its versatility and effectiveness across different domains .
Overall, the paper introduces a novel self-consistency framework based on MDL principles, presents methods for graph construction and hyperparameter selection, evaluates the model using base LLMs, and conducts detailed analyses for precision/recall in argument structure extraction, contributing significantly to the field of structured commonsense reasoning. The "MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning" paper introduces several characteristics and advantages compared to previous methods in the field of structured commonsense reasoning .
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Self-Consistency Framework: The paper proposes a self-consistency framework based on the Minimum Description Length (MDL) principle, which efficiently represents datasets using the fewest bits while minimizing model complexity. This approach assimilates relevant information from diverse structured responses without fine-tuning, constructing an aggregate graph that leverages the strengths of each sample .
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Graph Construction: The method described in the paper for constructing optimal aggregate graphs involves formulating an objective to determine the optimal values for nodes and edges in the graph. By examining consistent properties across samples, the approach improves recall for component and relation identification, addressing the issue of omitting true properties that may arise when relying on a single sample alone .
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Hyperparameter Selection: The paper discusses the automatic selection of hyperparameters {λ1, λ2} using k-fold cross-validation with few-shot examples. It evaluates the impact of varying hyperparameters on final performance and compares it with automatically estimated hyperparameters. The study shows that the automatic hyperparameter search reaches near-optimal performance for component identification, with room for improvement in relation prediction .
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Precision/Recall Analysis: The paper conducts a precision/recall analysis for argument structure extraction to demonstrate the effectiveness of the algorithm in reducing errors and capturing genuine properties from multiple samples. MIDGARD consistently improves recall for component and relation identification across all datasets by relying on multiple samples to formulate the final hypothesis, leading to improved precision for relation identification .
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Integration of Information: Unlike existing approaches that lack integration of information from different samples, MIDGARD assimilates relevant information from diverse structured responses without fine-tuning. By leveraging consistencies among samples, it constructs an aggregate graph that effectively captures genuine properties and reduces errors in structured commonsense reasoning tasks .
Overall, the characteristics of the MIDGARD framework include self-consistency based on MDL principles, optimal graph construction, automatic hyperparameter selection, precision/recall analysis for argument structure extraction, and effective integration of information from diverse samples, providing significant advantages in structured commonsense reasoning tasks compared to previous methods.
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research studies and notable researchers in the field of structured commonsense reasoning have been identified in the provided context:
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Related Research Studies:
- A study by Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant in 2021 titled "Did Aristotle use a laptop? a question answering benchmark with implicit reasoning strategies" .
- Research by Aman Madaan, Dheeraj Rajagopal, Niket Tandon, Yiming Yang, and Eduard Hovy in 2021 on "Generating inference graphs for defeasible reasoning" .
- Work by Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu in 2023 focusing on "Gptscore: Evaluate as you desire" .
- The study by Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen in 2023 titled "Critic: Large language models can self-correct with tool-interactive critiquing" .
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Noteworthy Researchers:
- Aman Madaan, who has contributed to various studies on structured reasoning tasks .
- Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant, who conducted research on question answering benchmarks with implicit reasoning strategies .
- Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu, who worked on evaluating models with Gptscore .
- Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen, who explored the self-correction capabilities of large language models .
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Key Solution Approach:
- The key solution approach mentioned in the paper involves utilizing the self-consistency (SC) strategy, which involves sampling diverse reasoning paths and taking a majority vote as the final answer. This method aims to increase confidence in the correctness of a consistent answer by constructing more accurate aggregate graphs through diverse sampling from large language models (LLMs) to alleviate error propagation in structured reasoning tasks .
How were the experiments in the paper designed?
The experiments in the paper were designed to evaluate the performance of the MIDGARD approach in structured reasoning tasks using large language models (LLMs) . The experiments involved comparing MIDGARD with other approaches like GREEDY and NUCLEUS decoding on tasks such as argument structure extraction . The experiments varied parameters such as the number of few-shot examples (N), hyperparameters λ1 and λ2, and the number of samples generated from the LLM . Performance metrics like component identification F1 score (C) and relation prediction metrics (R100, R50) were used to assess the effectiveness of the MIDGARD approach across different datasets and LLM models . The experiments aimed to demonstrate the superior performance of MIDGARD in structured reasoning tasks compared to other aggregation strategies, showcasing consistent performance improvements in component identification and relation prediction .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is the ESSAYS dataset . The availability of the code as open source was not explicitly mentioned in the provided context.
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The study focuses on enhancing the performance of structured reasoning tasks by generating task-specific graphs through self-consistency and aggregating multiple graph samples from Language Models (LLMs) . The approach demonstrated effectiveness across various structured commonsense reasoning tasks, showcasing the potential of the proposed methodology . Additionally, the experiments involved rigorous testing and comparison with existing techniques, such as GREEDY decoding, to evaluate the performance improvements achieved by the proposed approach . The results consistently show that MIDGARD outperforms GREEDY across different numbers of few-shot examples, indicating the validity and efficacy of the approach . Furthermore, the study addresses key challenges in structured reasoning tasks, such as style discrepancy and error propagation, by leveraging self-consistency strategies to sample diverse reasoning paths and construct accurate aggregate graphs, thereby mitigating error propagation issues . This innovative approach contributes significantly to advancing the field of structured commonsense reasoning by providing a robust methodology that can handle complex graph structures and specific task constraints effectively .
What are the contributions of this paper?
The paper makes several key contributions:
- Introduction of Self-Consistency (SC) Strategy: The paper introduces the Self-Consistency (SC) strategy, which involves sampling diverse reasoning paths and taking a majority vote as the final answer to enhance the correctness of consistent answers .
- Addressing Style Discrepancy and Error Propagation: It addresses challenges in structured reasoning tasks such as style discrepancy and error propagation. The COCOGEN approach is proposed to tackle style mismatch by using programming scripts as prompts for Large Language Models (LLMs) but still faces error propagation issues .
- Optimization with Minimum Description Length (MDL): The paper utilizes the Minimum Description Length (MDL) principle to find the optimal model that efficiently represents a dataset using the fewest bits while minimizing model complexity .
- Enhanced Graph Generation: The paper proposes iterative refinement with self-feedback to enhance graph generation, aiming to construct a more accurate aggregate graph and alleviate error propagation in structured reasoning tasks .
- Performance Evaluation: The paper evaluates the performance of the proposed MIDGARD approach on various datasets, showing consistent performance improvements in component identification and relation prediction across different datasets and Large Language Models (LLMs) .
What work can be continued in depth?
To delve deeper into structured commonsense reasoning tasks, further exploration can be conducted in the following areas based on the provided context:
- Addressing Style Discrepancy: Research can focus on overcoming style discrepancies in structured response generation by moving away from representing graphs as flattened strings, which can lead to performance issues due to output style mismatch .
- Mitigating Error Propagation: There is a need to develop strategies to minimize error propagation in autoregressive decoding processes, where incorrect decisions made earlier can impact later generation steps. This includes exploring methods to reduce the influence of errors in variable declarations and function calls that describe nodes and edges within graphs .
- Utilizing Self-Consistency Strategy: Further investigation into the self-consistency (SC) strategy, which involves sampling diverse reasoning paths and taking a majority vote for the final answer, can enhance the accuracy and confidence in consistent answers. This approach can be applied to construct more accurate aggregate graphs and reduce error propagation in structured reasoning tasks .
- Enhancing Graph Generation: Research can focus on improving semantic graph generation from natural language texts by extracting semantic structures represented as a list of edges. This includes exploring datasets like KELM and WEBNLG to evaluate the efficacy of various algorithms in automatically determining the order of operations to achieve specific goals .
- Optimizing Large Language Models: Further studies can investigate the challenges posed by the time complexity of ILP solvers and the limitations of utilizing LLMs effectively for commonsense reasoning tasks involving larger graphs. Modifications and strategies are needed to address these challenges and enhance the application of LLMs in generating graph structures .