Integrating Explanations in Learning LTL Specifications from Demonstrations

• URL: http://arxiv.org/abs/2404.02872v1
• Date: Wed, 3 Apr 2024 17:09:00 GMT
• Title: Integrating Explanations in Learning LTL Specifications from Demonstrations
• Authors: Ashutosh Gupta, John Komp, Abhay Singh Rajput, Krishna Shankaranarayanan, Ashutosh Trivedi, Namrita Varshney,
• Abstract summary: This paper investigates whether recent advances in Large Language Models (LLMs) can assist in translating human explanations into a format that can robustly support learning Linear Temporal Logic (LTL) from demonstrations. We present a principled approach combining LLMs and optimization-based methods to faithfully translate human explanations and demonstrations into specifications.
• Score: 6.070833893646998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates whether recent advances in Large Language Models (LLMs) can assist in translating human explanations into a format that can robustly support learning Linear Temporal Logic (LTL) from demonstrations. Both LLMs and optimization-based methods can extract LTL specifications from demonstrations; however, they have distinct limitations. LLMs can quickly generate solutions and incorporate human explanations, but their lack of consistency and reliability hampers their applicability in safety-critical domains. On the other hand, optimization-based methods do provide formal guarantees but cannot process natural language explanations and face scalability challenges. We present a principled approach to combining LLMs and optimization-based methods to faithfully translate human explanations and demonstrations into LTL specifications. We have implemented a tool called Janaka based on our approach. Our experiments demonstrate the effectiveness of combining explanations with demonstrations in learning LTL specifications through several case studies.

Related papers

• Large Language Models are Interpretable Learners [53.56735770834617]
  In this paper, we show a combination of Large Language Models (LLMs) and symbolic programs can bridge the gap between expressiveness and interpretability. The pretrained LLM with natural language prompts provides a massive set of interpretable modules that can transform raw input into natural language concepts. As the knowledge learned by LSP is a combination of natural language descriptions and symbolic rules, it is easily transferable to humans (interpretable) and other LLMs.
  arXiv  Detail & Related papers  (2024-06-25T02:18:15Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• LLMs for XAI: Future Directions for Explaining Explanations [50.87311607612179]
  We focus on refining explanations computed using existing XAI algorithms. Initial experiments and user study suggest that LLMs offer a promising way to enhance the interpretability and usability of XAI.
  arXiv  Detail & Related papers  (2024-05-09T19:17:47Z)
• Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models [0.8133739801185272]
  The alignments of reasoning abilities between smaller and larger Language Models are largely conducted via Supervised Fine-Tuning (SFT) We propose the Self-refine Instruction-tuning method that elicits Smaller Language Models to self-refine their abilities. Results obtained on commonsense and math reasoning tasks show that this approach significantly outperforms Instruction-tuning in both in-domain and out-domain scenarios.
  arXiv  Detail & Related papers  (2024-05-01T09:10:27Z)
• Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning [57.323716555996114]
  Off-target translation remains an unsolved problem, especially for low-resource languages. Recent works have either designed advanced prompting strategies to highlight the functionality of translation instructions or exploited the in-context learning ability of LLMs. In this work, we design a two-stage fine-tuning algorithm to improve the instruction-following ability (especially the translation direction) of LLMs.
  arXiv  Detail & Related papers  (2024-03-21T13:47:40Z)
• Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension [63.330262740414646]
  We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs) We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
  arXiv  Detail & Related papers  (2024-02-28T04:56:21Z)
• Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning [50.00090601424348]
  Large language models (LLMs) have shown remarkable capabilities in various natural language understanding tasks. We propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs.
  arXiv  Detail & Related papers  (2023-11-13T06:13:38Z)
• LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction [12.673710691468264]
  We introduce the Heuristic-Driven Link-of- Analogy (HD-LoA) prompting to address the challenge of example selection. Inspired by the analogical reasoning of human, we propose the link-of-analogy prompting, which enables LLMs to process new situations. Experiments show that our method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets.
  arXiv  Detail & Related papers  (2023-11-11T12:05:01Z)
• In-Context Explainers: Harnessing LLMs for Explaining Black Box Models [28.396104334980492]
  Large Language Models (LLMs) have demonstrated exceptional capabilities in complex tasks like machine translation, commonsense reasoning, and language understanding. One of the primary reasons for the adaptability of LLMs in such diverse tasks is their in-context learning (ICL) capability, which allows them to perform well on new tasks by simply using a few task samples in the prompt. We propose a novel framework, In-Context Explainers, comprising of three novel approaches that exploit the ICL capabilities of LLMs to explain the predictions made by other predictive models.
  arXiv  Detail & Related papers  (2023-10-09T15:31:03Z)
• Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal Demonstrations [6.950510860295866]
  We present a method for learning multi-stage tasks from demonstrations by learning the logical structure and atomic propositions of a consistent linear temporal logic (LTL) formula. The learner is given successful but potentially suboptimal demonstrations, where the demonstrator is optimizing a cost function while satisfying the formula, and the cost function is uncertain to the learner. Our algorithm uses the Karush-Kuhn-Tucker (KKT) optimality conditions of the demonstrations together with a counter-example-guided falsification strategy to learn the atomic proposition parameters.
  arXiv  Detail & Related papers  (2020-06-03T17:40:14Z)

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.