Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background


Method


Experiments
4.1 Multi-hop Reasoning Performance
4.2 Reasoning with Distractors
4.3 Generalization to Real-World knowledge
4.4 Run-time Analysis
4.5 Memorizing Knowledge


Related Work


Conclusion, Acknowledgements, and References Background Background Background Method Method Method Experiments
4.1 Multi-hop Reasoning Performance
4.2 Reasoning with Distractors
4.3 Generalization to Real-World knowledge
4.4 Run-time Analysis
4.5 Memorizing Knowledge Experiments Experiments 4.1 Multi-hop Reasoning Performance 4.1 Multi-hop Reasoning Performance 4.2 Reasoning with Distractors 4.2 Reasoning with Distractors 4.3 Generalization to Real-World knowledge 4.3 Generalization to Real-World knowledge 4.4 Run-time Analysis 4.4 Run-time Analysis 4.5 Memorizing Knowledge 4.5 Memorizing Knowledge Related Work Related Work Related Work Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References A. Dataset Dataset B. In-context Reasoning with Distractors B. In-context Reasoning with Distractors C. Implementation Details C. Implementation Details D. Adaptive Learning Rate D. Adaptive Learning Rate E. Experiments with Large Language Models E. Experiments with Large Language Models A Dataset ProofWriter The ProofWriter [73] dataset has 500k pairs of questions, answers, and proofs over natural-language rule bases. Each example in the dataset contains a set of facts, a set of rules, a hypothesis, and a label indicating whether the hypothesis is true, false, or unknown. The dataset comprise five datasets named D0, D1, D2, D3, D5, each with 100k examples. Each dataset’s questions require reasoning up to depths D (D = 0, 1, 2, 3, 5) to determine their answers. In our experiments, we only focus on the datasets that require more reasoning depths (D2, D3, D5). We show an example from the dataset in Table 7. In these datasets, a set of facts and rules are mapped to 18 questions, where the questions can be answered based on a subset of the facts and rules. Thus, some of the facts or rules can be irrelevant to some questions, and we call them distractors in Section 4.2. In the experiment for knowledge encoding with distractors, we encode all the facts in the model parameters and evaluate its ability to reproduce and reason over the correct facts. We show an example of distractor and relevant knowledge of a question in Table 9. For detailed statistics on the two datasets, please see Table 6. ProofWriter CLUTRR-SG The CLUTRR-SG [28] is an evaluation dataset for inductive reasoning on family relations adapted from the [71] dataset for measuring systematic generalization. Each example in the dataset contains (i) a set of facts representing a family graph G = (V, E) where nodes (V ) are entities and edges (E) are the relationships. (ii) a question asking the relationship between two entities (v1, vn ∈ V ), and (iii) a target relationship e ∗ ∈ E as the answer for the question. The facts are expressed as a list of (vi , ej , vk) tuples. The two entities in the question are separated by more than one hop in the graph. There are 272 unique entities, 20 relationship types, and nearly 1.5M possible facts in the dataset. Following the authors, we define the difficulty of examples based on the number of family graph edges (i.e., the number of reasoning hops required to determine a relation), in which k edges (k-hop) correspond to k facts. We show an example from the dataset in Table 8. CLUTRR-SG Authors:
(1) Zeming Chen, EPFL (zeming.chen@epfl.ch);
(2) Gail Weiss, EPFL (antoine.bosselut@epfl.ch);
(3) Eric Mitchell, Stanford University (eric.mitchell@cs.stanford.edu)';
(4) Asli Celikyilmaz, Meta AI Research (aslic@meta.com);
(5) Antoine Bosselut, EPFL (antoine.bosselut@epfl.ch). Authors: Authors: (1) Zeming Chen, EPFL (zeming.chen@epfl.ch); (2) Gail Weiss, EPFL (antoine.bosselut@epfl.ch); (3) Eric Mitchell, Stanford University (eric.mitchell@cs.stanford.edu)'; (4) Asli Celikyilmaz, Meta AI Research (aslic@meta.com); (5) Antoine Bosselut, EPFL (antoine.bosselut@epfl.ch). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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Evaluating Systematic Generalization: The Use of ProofWriter and CLUTRR-SG in LLM Reasoning Research

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