Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning

• URL: http://arxiv.org/abs/2205.09712v1
• Date: Thu, 19 May 2022 17:25:28 GMT
• Title: Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
• Authors: Antonia Creswell, Murray Shanahan and Irina Higgins
• Abstract summary: We show that language models tend to perform fairly well at single step inference tasks, but struggle to chain together multiple reasoning steps to solve more complex problems. We propose a Selection-Inference (SI) framework that exploits pre-trained LLMs as general processing modules. We show that a 7B parameter LLM used within the SI framework in a 5-shot generalisation setting, with no fine-tuning, yields a performance improvement of over 100%.
• Score: 14.663216851932646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks. However, they still tend to perform poorly on multi-step logical reasoning problems. Here we carry out a comprehensive evaluation of LLMs on 50 tasks that probe different aspects of logical reasoning. We show that language models tend to perform fairly well at single step inference or entailment tasks, but struggle to chain together multiple reasoning steps to solve more complex problems. In light of this, we propose a Selection-Inference (SI) framework that exploits pre-trained LLMs as general processing modules, and alternates between selection and inference to generate a series of interpretable, casual reasoning steps leading to the final answer. We show that a 7B parameter LLM used within the SI framework in a 5-shot generalisation setting, with no fine-tuning, yields a performance improvement of over 100% compared to an equivalent vanilla baseline on a suite of 10 logical reasoning tasks. The same model in the same setting even outperforms a significantly larger 280B parameter baseline on the same suite of tasks. Moreover, answers produced by the SI framework are accompanied by a causal natural-language-based reasoning trace, which has important implications for the safety and trustworthiness of the system.

Related papers

• Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models [84.15513004135576]
  Current research enhances the reasoning performance of Large Language Models (LLMs) by sampling multiple reasoning chains and ensembling based on the answer frequency. This approach fails in scenarios where the correct answers are in the minority. We introduce a hierarchical reasoning aggregation framework AoR, which selects answers based on the evaluation of reasoning chains.
  arXiv  Detail & Related papers  (2024-05-21T17:12:19Z)
• Self-Discover: Large Language Models Self-Compose Reasoning Structures [136.48389510481758]
  We introduce SELF-DISCOVER, a framework for self-discovering task-intrinsic reasoning structures. SELF-DISCOVER substantially improves GPT-4 and PaLM 2's performance on challenging reasoning benchmarks. We show that the self-discovered reasoning structures are universally applicable across model families.
  arXiv  Detail & Related papers  (2024-02-06T01:13:53Z)
• Can LLMs perform structured graph reasoning? [4.676784872259775]
  Pretrained Large Language Models (LLMs) have demonstrated various reasoning capabilities through language-based prompts alone. We design various graph reasoning tasks as a proxy to semi-structured tasks in this paper. We benchmark 5 different instruct-finetuned LLMs (GPT-4, GPT-3.5, Claude-2, Llama-2 and Palm-2) on the aforementioned tasks.
  arXiv  Detail & Related papers  (2024-02-02T09:45:33Z)
• LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models [63.14196038655506]
  We introduce LogicAsker, a novel approach for evaluating and enhancing the logical reasoning capabilities of large language models (LLMs) Our methodology reveals significant gaps in LLMs' learning of logical rules, with identified reasoning failures ranging from 29% to 90% across different models. We leverage these findings to construct targeted demonstration examples and fine-tune data, notably enhancing logical reasoning in models like GPT-4o by up to 5%.
  arXiv  Detail & Related papers  (2024-01-01T13:53:53Z)
• In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax [36.98247762224868]
  In-context learning (ICL) is now a common method for teaching large language models (LLMs) new tasks. Do models infer the underlying structure of the task defined by the context, or do they rely on superficial generalizations that only generalize to identically distributed examples? In experiments with models from the GPT, PaLM, and Llama 2 families, we find large variance across LMs. The variance is explained more by the composition of the pre-training corpus and supervision methods than by model size.
  arXiv  Detail & Related papers  (2023-11-13T23:52:43Z)
• Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text [5.532477732693001]
  We show that a large language model can serve as a highly effective few-shot semantically. It can convert natural language sentences into a logical form that serves as input for answer set programs. We demonstrate that this method achieves state-of-the-art performance on several benchmarks, including bAbI, StepGame, CLUTRR, and gSCAN.
  arXiv  Detail & Related papers  (2023-07-15T03:29:59Z)
• Exploring Self-supervised Logic-enhanced Training for Large Language Models [59.227222647741094]
  In this paper, we make the first attempt to investigate the feasibility of incorporating logical knowledge through self-supervised post-training. We devise an auto-regressive objective variant of MERIt and integrate it with two LLM series, i.e., FLAN-T5 and LLaMA, with parameter size ranging from 3 billion to 13 billion. The results on two challenging logical reasoning benchmarks demonstrate the effectiveness of LogicLLM.
  arXiv  Detail & Related papers  (2023-05-23T06:13:10Z)
• Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models [81.01397924280612]
  Large language models (LLMs) can achieve highly effective performance on various reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting as demonstrations. We introduce Iter-CoT (Iterative bootstrapping in Chain-of-Thoughts Prompting), an iterative bootstrapping approach for selecting exemplars and generating reasoning chains.
  arXiv  Detail & Related papers  (2023-04-23T13:54:39Z)
• PAL: Program-aided Language Models [112.94785609781503]
  We present Program-Aided Language models (PaL) to understand natural language problems. PaL offloads the solution step to a programmatic runtime such as a Python interpreter. We set new state-of-the-art results in all 12 benchmarks.
  arXiv  Detail & Related papers  (2022-11-18T18:56:13Z)
• ThinkSum: Probabilistic reasoning over sets using large language models [18.123895485602244]
  We propose a two-stage probabilistic inference paradigm, ThinkSum, which reasons over sets of objects or facts in a structured manner. We demonstrate the possibilities and advantages of ThinkSum on the BIG-bench suite of LLM evaluation tasks.
  arXiv  Detail & Related papers  (2022-10-04T00:34:01Z)

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.