MLLM-SR: Conversational Symbolic Regression base Multi-Modal Large Language Models

• URL: http://arxiv.org/abs/2406.05410v1
• Date: Sat, 8 Jun 2024 09:17:54 GMT
• Title: MLLM-SR: Conversational Symbolic Regression base Multi-Modal Large Language Models
• Authors: Yanjie Li, Weijun Li, Lina Yu, Min Wu, Jingyi Liu, Wenqiang Li, Shu Wei, Yusong Deng,
• Abstract summary: MLLM-SR is a conversational symbolic regression method that can generate expressions that meet the requirements simply by describing the requirements with natural language instructions. We experimentally demonstrate that MLLM-SR can well understand the prior knowledge we add to the natural language instructions.
• Score: 13.136507215114722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Formulas are the language of communication between humans and nature. It is an important research topic of artificial intelligence to find expressions from observed data to reflect the relationship between each variable in the data, which is called a symbolic regression problem. The existing symbolic regression methods directly generate expressions according to the given observation data, and we cannot require the algorithm to generate expressions that meet specific requirements according to the known prior knowledge. For example, the expression needs to contain $\sin$ or be symmetric, and so on. Even if it can, it often requires very complex operations, which is very inconvenient. In this paper, based on multi-modal large language models, we propose MLLM-SR, a conversational symbolic regression method that can generate expressions that meet the requirements simply by describing the requirements with natural language instructions. By experimenting on the Nguyen dataset, we can demonstrate that MLLM-SR leads the state-of-the-art baselines in fitting performance. More notably, we experimentally demonstrate that MLLM-SR can well understand the prior knowledge we add to the natural language instructions. Moreover, the addition of prior knowledge can effectively guide MLLM-SR to generate correct expressions.

Related papers

• Matchmaker: Self-Improving Large Language Model Programs for Schema Matching [60.23571456538149]
  We propose a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker self-improves in a zero-shot manner without the need for labeled demonstrations. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches.
  arXiv  Detail & Related papers  (2024-10-31T16:34:03Z)
• Language Representations Can be What Recommenders Need: Findings and Potentials [57.90679739598295]
  We show that item representations, when linearly mapped from advanced LM representations, yield superior recommendation performance. This outcome suggests the possible homomorphism between the advanced language representation space and an effective item representation space for recommendation. Our findings highlight the connection between language modeling and behavior modeling, which can inspire both natural language processing and recommender system communities.
  arXiv  Detail & Related papers  (2024-07-07T17:05:24Z)
• Verbalized Machine Learning: Revisiting Machine Learning with Language Models [63.10391314749408]
  We introduce the framework of verbalized machine learning (VML) VML constrains the parameter space to be human-interpretable natural language. We empirically verify the effectiveness of VML, and hope that VML can serve as a stepping stone to stronger interpretability.
  arXiv  Detail & Related papers  (2024-06-06T17:59:56Z)
• What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages [78.1866280652834]
  Large language models (LM) are distributions over strings. We investigate the learnability of regular LMs (RLMs) by RNN and Transformer LMs. We find that the complexity of the RLM rank is strong and significant predictors of learnability for both RNNs and Transformers.
  arXiv  Detail & Related papers  (2024-06-06T17:34:24Z)
• In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery [5.2387832710686695]
  In this work, we introduce the first comprehensive framework that utilizes Large Language Models (LLMs) for the task of Symbolic Regression. We propose In-Context Symbolic Regression (ICSR), an SR method which iteratively refines a functional form with an external LLM and determines its coefficients with an external LLM. Our findings reveal that LLMs are able to successfully find symbolic equations that fit the given data, matching or outperforming the overall performance of the best SR baselines on four popular benchmarks.
  arXiv  Detail & Related papers  (2024-04-29T20:19:25Z)
• LLM-SR: Scientific Equation Discovery via Programming with Large Language Models [17.64574496035502]
  Traditional methods of equation discovery, known as symbolic regression, largely focus on extracting equations from data alone. We introduce LLM-SR, a novel approach that leverages the scientific knowledge and robust code generation capabilities of Large Language Models. We demonstrate LLM-SR's effectiveness across three diverse scientific domains, where it discovers physically accurate equations.
  arXiv  Detail & Related papers  (2024-04-29T03:30:06Z)
• Large Language Models for Scientific Synthesis, Inference and Explanation [56.41963802804953]
  We show how large language models can perform scientific synthesis, inference, and explanation. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. This approach has the further advantage that the large language model can explain the machine learning system's predictions.
  arXiv  Detail & Related papers  (2023-10-12T02:17:59Z)
• Linearity of Relation Decoding in Transformer Language Models [82.47019600662874]
  Much of the knowledge encoded in transformer language models (LMs) may be expressed in terms of relations. We show that, for a subset of relations, this computation is well-approximated by a single linear transformation on the subject representation.
  arXiv  Detail & Related papers  (2023-08-17T17:59:19Z)
• Commonsense Knowledge Transfer for Pre-trained Language Models [83.01121484432801]
  We introduce commonsense knowledge transfer, a framework to transfer the commonsense knowledge stored in a neural commonsense knowledge model to a general-purpose pre-trained language model. It first exploits general texts to form queries for extracting commonsense knowledge from the neural commonsense knowledge model. It then refines the language model with two self-supervised objectives: commonsense mask infilling and commonsense relation prediction.
  arXiv  Detail & Related papers  (2023-06-04T15:44:51Z)
• Language Models as Inductive Reasoners [125.99461874008703]
  We propose a new paradigm (task) for inductive reasoning, which is to induce natural language rules from natural language facts. We create a dataset termed DEER containing 1.2k rule-fact pairs for the task, where rules and facts are written in natural language. We provide the first and comprehensive analysis of how well pretrained language models can induce natural language rules from natural language facts.
  arXiv  Detail & Related papers  (2022-12-21T11:12:14Z)
• Is neural language acquisition similar to natural? A chronological probing study [0.0515648410037406]
  We present the chronological probing study of transformer English models such as MultiBERT and T5. We compare the information about the language learned by the models in the process of training on corpora. The results show that 1) linguistic information is acquired in the early stages of training 2) both language models demonstrate capabilities to capture various features from various levels of language.
  arXiv  Detail & Related papers  (2022-07-01T17:24:11Z)
• Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization [20.14487209460865]
  We investigate four translation methods that can translate natural questions into cloze-style sentences. We show that our methods are complementary datasets to a knowledge base improved model, and combining them can lead to state-of-the-art zero-shot performance.
  arXiv  Detail & Related papers  (2022-01-01T07:12:49Z)
• Generated Knowledge Prompting for Commonsense Reasoning [53.88983683513114]
  We propose generating knowledge statements directly from a language model with a generic prompt format. This approach improves performance of both off-the-shelf and finetuned language models on four commonsense reasoning tasks. Notably, we find that a model's predictions can improve when using its own generated knowledge.
  arXiv  Detail & Related papers  (2021-10-15T21:58:03Z)
• Learning to Ask Conversational Questions by Optimizing Levenshtein Distance [83.53855889592734]
  We introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimize the minimum Levenshtein distance (MLD) through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-30T08:44:19Z)
• Neural Language Generation: Formulation, Methods, and Evaluation [13.62873478165553]
  Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans. High capacity deep learning models trained on large scale datasets demonstrate unparalleled abilities to learn patterns in the data even in the lack of explicit supervision signals. There is no standard way to assess the quality of text produced by these generative models, which constitutes a serious bottleneck towards the progress of the field.
  arXiv  Detail & Related papers  (2020-07-31T00:08:28Z)
• Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge [96.92252296244233]
  Large pre-trained language models (LMs) acquire some reasoning capacity, but this ability is difficult to control. We show that LMs can be trained to reliably perform systematic reasoning combining both implicit, pre-trained knowledge and explicit natural language statements. Our work paves a path towards open-domain systems that constantly improve by interacting with users who can instantly correct a model by adding simple natural language statements.
  arXiv  Detail & Related papers  (2020-06-11T17:02:20Z)
• Information-Theoretic Probing for Linguistic Structure [74.04862204427944]
  We propose an information-theoretic operationalization of probing as estimating mutual information. We evaluate on a set of ten typologically diverse languages often underrepresented in NLP research.
  arXiv  Detail & Related papers  (2020-04-07T01:06:36Z)

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.