Search-based Optimisation of LLM Learning Shots for Story Point Estimation

• URL: http://arxiv.org/abs/2403.08430v1
• Date: Wed, 13 Mar 2024 11:29:37 GMT
• Title: Search-based Optimisation of LLM Learning Shots for Story Point Estimation
• Authors: Vali Tawosi, Salwa Alamir, Xiaomo Liu
• Abstract summary: We use Search-Based methods to optimise the number and combination of examples that can improve an LLM's estimation performance. Our preliminary results show that our SBSE technique improves the estimation performance of the LLM by 59.34% on average.
• Score: 3.5365325264937897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the ways Large Language Models (LLMs) are used to perform machine learning tasks is to provide them with a few examples before asking them to produce a prediction. This is a meta-learning process known as few-shot learning. In this paper, we use available Search-Based methods to optimise the number and combination of examples that can improve an LLM's estimation performance, when it is used to estimate story points for new agile tasks. Our preliminary results show that our SBSE technique improves the estimation performance of the LLM by 59.34% on average (in terms of mean absolute error of the estimation) over three datasets against a zero-shot setting.

Related papers

• Efficient Evaluation of Large Language Models via Collaborative Filtering [25.734508624520164]
  Large Language Models (LLMs) have been proposed to measure and compare the capabilities of different LLMs. evaluating LLMs is costly due to the large number of test instances and their slow inference speed. We propose a two-stage method to efficiently estimate a model's real performance on a given benchmark.
  arXiv  Detail & Related papers  (2025-04-05T07:46:30Z)
• Efficient Model Selection for Time Series Forecasting via LLMs [52.31535714387368]
  We propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection. Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-04-02T20:33:27Z)
• Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMs [0.464982780843177]
  This work investigates the ability of open Large Language Models (LLMs) to predict citation intent through in-context learning and fine-tuning. We evaluate twelve model variations across five prominent open LLM families using zero, one, few, and many-shot prompting to assess performance across scenarios. The results highlight the strengths and limitations of LLMs in recognizing citation intents, providing valuable insights for model selection and prompt engineering.
  arXiv  Detail & Related papers  (2025-02-20T13:45:42Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• STAR: A Simple Training-free Approach for Recommendations using Large Language Models [36.18841135511487]
  Recent progress in large language models (LLMs) offers promising new approaches for recommendation system (RecSys) tasks. We propose a framework that utilizes LLMs and can be applied to various recommendation tasks without the need for fine-tuning. Our method achieves Hits@10 performance of +23.8% on Beauty, +37.5% on Toys and Games, and -1.8% on Sports and Outdoors.
  arXiv  Detail & Related papers  (2024-10-21T19:34:40Z)
• Scaling Laws for Predicting Downstream Performance in LLMs [75.28559015477137]
  This work focuses on the pre-training loss as a more-efficient metric for performance estimation. We extend the power law analytical function to predict domain-specific pre-training loss based on FLOPs across data sources. We employ a two-layer neural network to model the non-linear relationship between multiple domain-specific loss and downstream performance.
  arXiv  Detail & Related papers  (2024-10-11T04:57:48Z)
• Achieving Peak Performance for Large Language Models: A Systematic Review [0.0]
  Large language models (LLMs) have achieved remarkable success in natural language processing (NLP) As models grow into the trillion- parameter range, computational and memory costs increase significantly. This makes it difficult for many researchers to access the resources needed to train or apply these models.
  arXiv  Detail & Related papers  (2024-09-07T13:57:41Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning [93.96463520716759]
  Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and hallucinations. Here, we introduce AvaTaR, a novel and automated framework that optimize an LLM agent to effectively leverage provided tools, improving performance on a given task.
  arXiv  Detail & Related papers  (2024-06-17T04:20:02Z)
• Large Language Model Enhanced Machine Learning Estimators for Classification [24.391150322835713]
  Pre-trained large language models (LLM) have emerged as a powerful tool for simulating various scenarios. We propose a few approaches to integrate LLM into a classical machine learning estimator to further enhance the prediction performance.
  arXiv  Detail & Related papers  (2024-05-08T22:28:57Z)
• Improve Temporal Awareness of LLMs for Sequential Recommendation [61.723928508200196]
  Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data. We propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation.
  arXiv  Detail & Related papers  (2024-05-05T00:21:26Z)
• Evaluating Large Language Models for Health-Related Text Classification Tasks with Public Social Media Data [3.9459077974367833]
  Large language models (LLMs) have demonstrated remarkable success in NLP tasks. We benchmarked one supervised classic machine learning model based on Support Vector Machines (SVMs), three supervised pretrained language models (PLMs) based on RoBERTa, BERTweet, and SocBERT, and two LLM based classifiers (GPT3.5 and GPT4), across 6 text classification tasks. Our comprehensive experiments demonstrate that employ-ing data augmentation using LLMs (GPT-4) with relatively small human-annotated data to train lightweight supervised classification models achieves superior results compared to training with human-annotated data
  arXiv  Detail & Related papers  (2024-03-27T22:05:10Z)
• LLM-augmented Preference Learning from Natural Language [19.700169351688768]
  Large Language Models (LLMs) are equipped to deal with larger context lengths. LLMs can consistently outperform the SotA when the target text is large. Few-shot learning yields better performance than zero-shot learning.
  arXiv  Detail & Related papers  (2023-10-12T17:17:27Z)
• Evaluating and Explaining Large Language Models for Code Using Syntactic Structures [74.93762031957883]
  This paper introduces ASTxplainer, an explainability method specific to Large Language Models for code. At its core, ASTxplainer provides an automated method for aligning token predictions with AST nodes. We perform an empirical evaluation on 12 popular LLMs for code using a curated dataset of the most popular GitHub projects.
  arXiv  Detail & Related papers  (2023-08-07T18:50:57Z)
• Efficient Nearest Neighbor Language Models [114.40866461741795]
  Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore. We show how to achieve up to a 6x speed-up in inference speed while retaining comparable performance.
  arXiv  Detail & Related papers  (2021-09-09T12:32:28Z)

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.