Verbalized Probabilistic Graphical Modeling with Large Language Models

• URL: http://arxiv.org/abs/2406.05516v1
• Date: Sat, 8 Jun 2024 16:35:31 GMT
• Title: Verbalized Probabilistic Graphical Modeling with Large Language Models
• Authors: Hengguan Huang, Xing Shen, Songtao Wang, Dianbo Liu, Hao Wang,
• Abstract summary: This work introduces a novel Bayesian prompting approach that facilitates training-free Bayesian inference with large language models. Our results indicate that the model effectively enhances confidence elicitation and text generation quality, demonstrating its potential to improve AI language understanding systems.
• Score: 8.961720262676195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Faced with complex problems, the human brain demonstrates a remarkable capacity to transcend sensory input and form latent understandings of perceived world patterns. However, this cognitive capacity is not explicitly considered or encoded in current large language models (LLMs). As a result, LLMs often struggle to capture latent structures and model uncertainty in complex compositional reasoning tasks. This work introduces a novel Bayesian prompting approach that facilitates training-free Bayesian inference with LLMs by using a verbalized Probabilistic Graphical Model (PGM). While traditional Bayesian approaches typically depend on extensive data and predetermined mathematical structures for learning latent factors and dependencies, our approach efficiently reasons latent variables and their probabilistic dependencies by prompting LLMs to adhere to Bayesian principles. We evaluated our model on several compositional reasoning tasks, both close-ended and open-ended. Our results indicate that the model effectively enhances confidence elicitation and text generation quality, demonstrating its potential to improve AI language understanding systems, especially in modeling uncertainty.

Related papers

• Exploring the Potential for Large Language Models to Demonstrate Rational Probabilistic Beliefs [12.489784979345654]
  We show that current versions of large language models (LLMs) lack the ability to provide rational and coherent representations of probabilistic beliefs. We apply well-established techniques for uncertainty quantification to measure the ability of LLM's to adhere to fundamental properties of probabilistic reasoning.
  arXiv  Detail & Related papers  (2025-04-18T11:50:30Z)
• On the Reasoning Capacity of AI Models and How to Quantify It [0.0]
  Large Language Models (LLMs) have intensified the debate surrounding the fundamental nature of their reasoning capabilities. While achieving high performance on benchmarks such as GPQA and MMLU, these models exhibit limitations in more complex reasoning tasks. We propose a novel phenomenological approach that goes beyond traditional accuracy metrics to probe the underlying mechanisms of model behavior.
  arXiv  Detail & Related papers  (2025-01-23T16:58:18Z)
• Self-Improvement in Language Models: The Sharpening Mechanism [70.9248553790022]
  We offer a new perspective on the capabilities of self-improvement through a lens we refer to as sharpening. Motivated by the observation that language models are often better at verifying response quality than they are at generating correct responses, we formalize self-improvement as using the model itself as a verifier during post-training. We analyze two natural families of self-improvement algorithms based on SFT and RLHF.
  arXiv  Detail & Related papers  (2024-12-02T20:24:17Z)
• On the Modeling Capabilities of Large Language Models for Sequential Decision Making [52.128546842746246]
  Large pretrained models are showing increasingly better performance in reasoning and planning tasks. We evaluate their ability to produce decision-making policies, either directly, by generating actions, or indirectly. In environments with unfamiliar dynamics, we explore how fine-tuning LLMs with synthetic data can significantly improve their reward modeling capabilities.
  arXiv  Detail & Related papers  (2024-10-08T03:12:57Z)
• Unlocking Structured Thinking in Language Models with Cognitive Prompting [0.0]
  We propose cognitive prompting as a novel approach to guide problem-solving in large language models. We evaluate the effectiveness of cognitive prompting on Meta's LLaMA models.
  arXiv  Detail & Related papers  (2024-10-03T19:53:47Z)
• TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimation [0.0]
  TokenSHAP is a novel method for interpreting large language models. It adapts Shapley values from cooperative game theory to natural language processing. It provides interpretable, quantitative measures of token importance.
  arXiv  Detail & Related papers  (2024-07-14T08:07:50Z)
• 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)
• An LLM Feature-based Framework for Dialogue Constructiveness Assessment [8.87747076871578]
  Research on dialogue constructiveness assessment focuses on (i) analysing conversational factors that influence individuals to take specific actions, win debates, change their perspectives or broaden their open-mindedness and (ii) predicting constructiveness outcomes following dialogues for such use cases. These objectives can be achieved by training either interpretable feature-based models or neural models such as pre-trained language models. We propose an LLM feature-based framework for dialogue constructiveness assessment that combines the strengths of feature-based and neural approaches.
  arXiv  Detail & Related papers  (2024-06-20T22:10:52Z)
• Self-supervised Interpretable Concept-based Models for Text Classification [9.340843984411137]
  This paper proposes a self-supervised Interpretable Concept Embedding Models (ICEMs) We leverage the generalization abilities of Large-Language Models to predict the concepts labels in a self-supervised way. ICEMs can be trained in a self-supervised way achieving similar performance to fully supervised concept-based models and end-to-end black-box ones.
  arXiv  Detail & Related papers  (2024-06-20T14:04:53Z)
• Can I understand what I create? Self-Knowledge Evaluation of Large Language Models [31.85129258347539]
  Large language models (LLMs) have achieved remarkable progress in linguistic tasks. Inspired by Feynman's principle of understanding through creation, we introduce a self-knowledge evaluation framework.
  arXiv  Detail & Related papers  (2024-06-10T09:53:54Z)
• Interpreting Pretrained Language Models via Concept Bottlenecks [55.47515772358389]
  Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. The lack of interpretability due to their black-box'' nature poses challenges for responsible implementation. We propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans.
  arXiv  Detail & Related papers  (2023-11-08T20:41:18Z)
• A Novel Neural-symbolic System under Statistical Relational Learning [50.747658038910565]
  We propose a general bi-level probabilistic graphical reasoning framework called GBPGR. In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models. Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
  arXiv  Detail & Related papers  (2023-09-16T09:15:37Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Explainability for Large Language Models: A Survey [59.67574757137078]
  Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. This paper introduces a taxonomy of explainability techniques and provides a structured overview of methods for explaining Transformer-based language models.
  arXiv  Detail & Related papers  (2023-09-02T22:14:26Z)
• UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning [86.91893533388628]
  This paper presents UniDiff, a unified multi-modal model that integrates image-text contrastive learning (ITC), text-conditioned image synthesis learning (IS), and reciprocal semantic consistency modeling (RSC) UniDiff demonstrates versatility in both multi-modal understanding and generative tasks.
  arXiv  Detail & Related papers  (2023-06-01T15:39:38Z)
• Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models [0.5801044612920815]
  This paper proposes a framework that expands the potential of data-driven approaches for DCM. It includes pseudo data samples that represent required relationships and a loss function that measures their fulfillment. A case study demonstrates the potential of this framework for discrete choice analysis.
  arXiv  Detail & Related papers  (2023-05-30T12:53:55Z)
• Explaining Language Models' Predictions with High-Impact Concepts [11.47612457613113]
  We propose a complete framework for extending concept-based interpretability methods to NLP. We optimize for features whose existence causes the output predictions to change substantially. Our method achieves superior results on predictive impact, usability, and faithfulness compared to the baselines.
  arXiv  Detail & Related papers  (2023-05-03T14:48:27Z)
• ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT [72.83383437501577]
  Large language models (LLMs) have recently demonstrated significant potential in mathematical abilities. LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities. This paper presents a novel method for integrating LLMs into the abductive learning framework.
  arXiv  Detail & Related papers  (2023-04-21T16:23:47Z)
• Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning [56.50123642237106]
  Common practice in model-based reinforcement learning is to learn models that model every aspect of the agent's environment. We argue that such models are not particularly well-suited for performing scalable and robust planning in lifelong reinforcement learning scenarios. We propose new kinds of models that only model the relevant aspects of the environment, which we call "minimal value-minimal partial models"
  arXiv  Detail & Related papers  (2023-01-24T16:40:01Z)
• Autoregressive Structured Prediction with Language Models [73.11519625765301]
  We describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs. Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at.
  arXiv  Detail & Related papers  (2022-10-26T13:27:26Z)

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.