RECORD
ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT
Computation and Language · Submitted 21 Apr 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to incompatibility of the underlying information flow among them, making it challenging to accomplish tasks autonomously. On the other hand, abductive learning (ABL) frameworks for integrating the two abilities of perception and reasoning has seen significant success in inverse decipherment of incomplete facts, but it is limited by the lack of semantic understanding of logical reasoning rules and the dependence on complicated domain knowledge representation. This paper presents a novel method (ChatABL) for integrating LLMs into the ABL framework, aiming at unifying the three abilities in a more user-friendly and understandable manner. The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format. Similarly, perceptual module provides necessary reasoning examples for LLMs in natural language format. The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies. To our best knowledge, the proposed ChatABL is the first attempt to explore a new pattern for further approaching human-level cognitive ability via natural language interaction with ChatGPT.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. | background 1.00 | 0.05 | 0.03 | asserted 0.95 |
| s1 | However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to incompatibility of the underlying information flow among them, making it challenging to accomplish tasks autonomously. | background 0.59 | 0.08 | 0.02 | asserted 0.99 |
| s2 | On the other hand, abductive learning (ABL) frameworks for integrating the two abilities of perception and reasoning has seen significant success in inverse decipherment of incomplete facts, but it is limited by the lack of semantic understanding of logical reasoning rules and the dependence on complicated domain knowledge representation. | background 0.66 | 0.04 | 0.03 | asserted 1.00 |
| s3 | This paper presents a novel method (ChatABL) for integrating LLMs into the ABL framework, aiming at unifying the three abilities in a more user-friendly and understandable manner. | contribution 1.00 | 0.84 | 0.16 | asserted 0.98 |
| s4 | The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format. | method 0.99 | 0.78 | 0.11 | asserted 1.00 |
| s5 | Similarly, perceptual module provides necessary reasoning examples for LLMs in natural language format. | method 0.98 | 0.50 | 0.04 | asserted 1.00 |
| s6 | The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies. | result 0.85 | 0.88 | 0.57 | asserted 1.00 |
| s7 | To our best knowledge, the proposed ChatABL is the first attempt to explore a new pattern for further approaching human-level cognitive ability via natural language interaction with ChatGPT. | contribution 0.88 | 0.56 | 0.17 | asserted 0.95 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to incompatibility of the underlying information flow among them, making it challenging to accomplish tasks autonomously. On the other hand, abductive learning (ABL) frameworks for integrating the two abilities of perception and reasoning has seen significant success in inverse decipherment of incomplete facts, but it is limited by the lack of semantic understanding of logical reasoning rules and the dependence on complicated domain knowledge representation. This paper presents a novel method (ChatABL) for integrating LLMs into the ABL framework, aiming at unifying the three abilities in a more user-friendly and understandable manner. The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format. Similarly, perceptual module provides necessary reasoning examples for LLMs in natural language format. The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies. To our best knowledge, the proposed ChatABL is the first attempt to explore a new pattern for further approaching human-level cognitive ability via natural language interaction with ChatGPT.
Human review
References
How the literature uses it
19 arXiv papers cite this in 21 sentences
Papers citing it · 14 sentences without a confident label
Compares with it 1 paper
-
Our method provides a more scalable approach to dataset augmentation through abductive logic compared to previous methods that require hand-crafted symbolic knowledge bases [ref] .
Data-Centric Financial Large Language Models · Abductive Reasoning · 0.81
Cites it as background 6 papers
-
Transformer-based [ref] models have significantly advanced [ref] the fields of natural language processing (NLP), computer vision (CV), and other application domains, laying the groundwork for the development of multimodal models such as LLaVA [ref] and GPT-4V.
On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications · LLMs and Multimodal Models · 0.96
-
Serving as a guiding light in natural language processing (NLP) [ref] , healthcare [ref] , clinical textual processing [ref] , biomedical image analyses [ref] , code generation [ref] , decision support [ref] , multimodal data analytics [ref] , and mathematical reasoning [ref] , LLMs push the boundaries of AI capabilities, approximating human-like reasoning through sophisticated statistical inference [ref] .
Evaluating Mathematical Reasoning Across Large Language Models: A Fine-Grained Approach · Introduction · 0.85
-
Beyond commonsense scenarios, abductive reasoning has also been explored in formal textual reasoning benchmarks such as ProofWriter [ref] , open-world reasoning with LLMs [ref] , abstract logical reasoning [ref] , and neuro-symbolic abductive learning [ref] .
One Hypothesis Is Not Enough: Abductive Reasoning with Agentic Hypothesis Refinement over Knowledge Graphs · Related Work · 0.99
What it cites · 25 sentences without a confident label
Uses the method or tool of 1 paper
-
In this study, we employ the Vision Transformer (ViT) [ref] as the perception model to process the handwritten inputs.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale · Perception Model · 1.00
Uses data or values from 1 paper
-
We construct a sequence of raw images of digits and operators to the hand writing equations based benchmark handwritten character datasets [ref] , as shown in Figure [ref].
The HASYv2 dataset · Experiment Setup · 0.94
Compares with 1 paper
-
We evaluate the learning performance of ChatABL by benchmarking it against Meet [12], CNN-BiLSTM [ref] , Transformer(TF) [ref] , and ABL [ref] , which involves pure perception methods (the former three), perception and reasoning methods (the last one).
Tunneling Neural Perception and Logic Reasoning through Abductive Learning · Experiment Setup · 0.97
Cites as background 14 papers
-
Recently, Wu et al. [ref] compared the deductive reasoning abilities of large language models.
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task · Reasoning via LLM · 0.86
-
As one of the most influential LLMs today, ChatGPT provides a user-friendly human-machine interaction platform and API that brings the powerful capabilities of large language models to the public and has been rapidly integrated into various fields of application such as education, healthcare, and others to perform general natural language processing tasks [ref] , including text classification, data augmentation, arithmetic reasoning, sentiment analysis, question and answering, summarization, and more.
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models · Large Language Models and ChatGPT · 0.98
-
As one of the most influential LLMs today, ChatGPT provides a user-friendly human-machine interaction platform and API that brings the powerful capabilities of large language models to the public and has been rapidly integrated into various fields of application such as education, healthcare, and others to perform general natural language processing tasks [ref] , including text classification, data augmentation, arithmetic reasoning, sentiment analysis, question and answering, summarization, and more.
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models · Large Language Models and ChatGPT · 0.98
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.