Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?

• URL: http://arxiv.org/abs/2411.17927v1
• Date: Tue, 26 Nov 2024 22:48:55 GMT
• Title: Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?
• Authors: Conor O'Brien, Daniel Rodriguez-Cardenas, Alejandro Velasco, David N. Palacio, Denys Poshyvanyk,
• Abstract summary: We investigate the emergence of capabilities in the context of Software Engineering. We propose a model-agnostic pipeline for evaluating this phenomenon across three SE tasks: bug fixing, code translation, and commit message generation. Our findings do not provide evidence to support the idea of emergent capabilities resulting from scaling the model size in the selected set of tasks.
• Score: 45.126233498200534
• License:
• Abstract: The adoption of Large Language Models (LLMs) across multiple contexts has sparked interest in understanding how scaling model size might lead to behavioral changes, as LLMs can exhibit behaviors not observed in their smaller counterparts. Understanding these emergent capabilities is essential for advancing LLM development and improving their interpretability across diverse tasks. However, whether LLMs exhibit true emergence in the context of Software Engineering remains an unexplored topic, as most research has focused on NLP tasks. In this paper, we investigate the emergence of capabilities in the context of SE. We propose a model-agnostic pipeline for evaluating this phenomenon across three SE tasks: bug fixing, code translation, and commit message generation. More precisely, for each task, we present a case study instantiating our pipeline to analyze the emergence of capabilities in CodeGen1-multi across four scales ranging from 350M to 16.1B parameters. Our findings do not not provide evidence to support the idea of emergent capabilities resulting from scaling the model size in the selected set of tasks. We hope our results can pave the way to a more nuanced understanding of emergent capabilities of LLMs within the SE domain, guiding future research to focus on task-specific evaluations and the identification of alternative factors contributing to this phenomenon. Our work underscores the importance of task diversity in examining model behaviors and highlights potential limitations in transferring prior understandings of and approaches to emergence from NLP to Software Engineering.

Related papers

• A Survey on Large Language Models with some Insights on their Capabilities and Limitations [0.3222802562733786]
  Large Language Models (LLMs) exhibit remarkable performance across various language-related tasks. LLMs have demonstrated emergent abilities extending beyond their core functions. This paper explores the foundational components, scaling mechanisms, and architectural strategies that drive these capabilities.
  arXiv  Detail & Related papers  (2025-01-03T21:04:49Z)
• Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models [22.676688441884465]
  Fine-tuning pre-trained large language models (LLMs) on a diverse array of tasks has become a common approach for building models. This study investigates the task-specific information encoded in pre-trained LLMs and the effects of instruction tuning on their representations.
  arXiv  Detail & Related papers  (2024-10-25T23:38:28Z)
• Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability [12.349247962800813]
  Large language models (LLMs) have emerged as powerful tools for many AI problems. They exhibit remarkable in-context learning (ICL) capabilities. How they approach composite tasks remains an open and largely underexplored question.
  arXiv  Detail & Related papers  (2024-07-22T15:22:34Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset [50.36095192314595]
  Large Language Models (LLMs) function as conscious agents with generalizable reasoning capabilities. This ability remains underexplored due to the complexity of modeling infinite possible changes in an event. We introduce the first-ever benchmark, MARS, comprising three tasks corresponding to each step.
  arXiv  Detail & Related papers  (2024-06-04T08:35:04Z)
• Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism [7.479892725446205]
  Multi-task learning (MTL) is a paradigm that simultaneously learns multiple tasks by sharing information at different levels. We introduce a posteriori information into the model, considering that different tasks may produce correlated outputs with mutual influences. We achieve this by incorporating a feedback mechanism into MTL models, where the output of one task serves as a hidden feature for another task.
  arXiv  Detail & Related papers  (2024-04-01T03:27:34Z)
• Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models [87.47400128150032]
  We propose a novel LMM architecture named Lumen, a Large multimodal model with versatile vision-centric capability enhancement. Lumen first promotes fine-grained vision-language concept alignment. Then the task-specific decoding is carried out by flexibly routing the shared representation to lightweight task decoders.
  arXiv  Detail & Related papers  (2024-03-12T04:13:45Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• 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)

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.