Emergent Representations of Program Semantics in Language Models Trained on Programs

• URL: http://arxiv.org/abs/2305.11169v3
• Date: Fri, 2 Aug 2024 23:09:32 GMT
• Title: Emergent Representations of Program Semantics in Language Models Trained on Programs
• Authors: Charles Jin, Martin Rinard,
• Abstract summary: We present evidence that language models (LMs) of code can learn to represent the formal semantics of programs. We train a Transformer model on a synthetic corpus of programs written in a domain-specific language for navigating 2D grid world environments.
• Score: 3.376269351435396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present evidence that language models (LMs) of code can learn to represent the formal semantics of programs, despite being trained only to perform next-token prediction. Specifically, we train a Transformer model on a synthetic corpus of programs written in a domain-specific language for navigating 2D grid world environments. Each program in the corpus is preceded by a (partial) specification in the form of several input-output grid world states. Despite providing no further inductive biases, we find that a probing classifier is able to extract increasingly accurate representations of the unobserved, intermediate grid world states from the LM hidden states over the course of training, suggesting the LM acquires an emergent ability to interpret programs in the formal sense. We also develop a novel interventional baseline that enables us to disambiguate what is represented by the LM as opposed to learned by the probe. We anticipate that this technique may be generally applicable to a broad range of semantic probing experiments. In summary, this paper does not propose any new techniques for training LMs of code, but develops an experimental framework for and provides insights into the acquisition and representation of formal semantics in statistical models of code. Our code is available at https://github.com/charlesjin/emergent-semantics.

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)
• Code Representation Pre-training with Complements from Program Executions [29.148208436656216]
  We propose FuzzPretrain to explore the dynamic information of programs revealed by their test cases and embed it into the feature representations of code as complements. FuzzyPretrain yielded more than 6%/9% mAP improvements on code search over its counterparts trained with only source code or AST.
  arXiv  Detail & Related papers  (2023-09-04T01:57:22Z)
• An Overview on Language Models: Recent Developments and Outlook [32.528770408502396]
  Conventional language models (CLMs) aim to predict the probability of linguistic sequences in a causal manner. Pre-trained language models (PLMs) cover broader concepts and can be used in both causal sequential modeling and fine-tuning for downstream applications.
  arXiv  Detail & Related papers  (2023-03-10T07:55:00Z)
• Language Model Pre-Training with Sparse Latent Typing [66.75786739499604]
  We propose a new pre-training objective, Sparse Latent Typing, which enables the model to sparsely extract sentence-level keywords with diverse latent types. Experimental results show that our model is able to learn interpretable latent type categories in a self-supervised manner without using any external knowledge.
  arXiv  Detail & Related papers  (2022-10-23T00:37:08Z)
• Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages [86.08359401867577]
  Back-translation is widely known for its effectiveness for neural machine translation when little to no parallel data is available. We propose performing back-translation via code summarization and generation. We show that our proposed approach performs competitively with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-23T08:20:41Z)
• Pre-Trained Language Models for Interactive Decision-Making [72.77825666035203]
  We describe a framework for imitation learning in which goals and observations are represented as a sequence of embeddings. We demonstrate that this framework enables effective generalization across different environments. For test tasks involving novel goals or novel scenes, initializing policies with language models improves task completion rates by 43.6%.
  arXiv  Detail & Related papers  (2022-02-03T18:55:52Z)
• How could Neural Networks understand Programs? [67.4217527949013]
  It is difficult to build a model to better understand programs, by either directly applying off-the-shelf NLP pre-training techniques to the source code, or adding features to the model by theshelf. We propose a novel program semantics learning paradigm, that the model should learn from information composed of (1) the representations which align well with the fundamental operations in operational semantics, and (2) the information of environment transition.
  arXiv  Detail & Related papers  (2021-05-10T12:21:42Z)
• Learning Universal Representations from Word to Sentence [89.82415322763475]
  This work introduces and explores the universal representation learning, i.e., embeddings of different levels of linguistic unit in a uniform vector space. We present our approach of constructing analogy datasets in terms of words, phrases and sentences. We empirically verify that well pre-trained Transformer models incorporated with appropriate training settings may effectively yield universal representation.
  arXiv  Detail & Related papers  (2020-09-10T03:53:18Z)
• Sequence Model Design for Code Completion in the Modern IDE [3.4824234779710452]
  We propose a novel design for predicting top-k next tokens that combines static analysis' ability to enumerate all valid keywords and in-scope identifiers with the ability of a language model to place a probability distribution over them. Our model mixes character-level input representation with token output to represent out-of-vocabulary (OOV) tokens meaningfully and minimize prediction latency.
  arXiv  Detail & Related papers  (2020-04-10T22:40:49Z)

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.