Effective Context Selection in LLM-based Leaderboard Generation: An Empirical Study

• URL: http://arxiv.org/abs/2407.02409v1
• Date: Thu, 6 Jun 2024 06:05:39 GMT
• Title: Effective Context Selection in LLM-based Leaderboard Generation: An Empirical Study
• Authors: Salomon Kabongo, Jennifer D'Souza, Sören Auer,
• Abstract summary: This paper explores the impact of context selection on the efficiency of Large Language Models (LLMs) in generating AI research leaderboards. We introduce a novel method that surpasses traditional Natural Language Inference (NLI) approaches in adapting to new developments without a predefined taxonomy.
• Score: 0.3072340427031969
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the impact of context selection on the efficiency of Large Language Models (LLMs) in generating Artificial Intelligence (AI) research leaderboards, a task defined as the extraction of (Task, Dataset, Metric, Score) quadruples from scholarly articles. By framing this challenge as a text generation objective and employing instruction finetuning with the FLAN-T5 collection, we introduce a novel method that surpasses traditional Natural Language Inference (NLI) approaches in adapting to new developments without a predefined taxonomy. Through experimentation with three distinct context types of varying selectivity and length, our study demonstrates the importance of effective context selection in enhancing LLM accuracy and reducing hallucinations, providing a new pathway for the reliable and efficient generation of AI leaderboards. This contribution not only advances the state of the art in leaderboard generation but also sheds light on strategies to mitigate common challenges in LLM-based information extraction.

Related papers

• Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization [0.27624021966289597]
  This paper introduces EYEGLAXS, a framework that leverages Large Language Models (LLMs) for extractive summarization. EYEGLAXS focuses on extractive summarization to ensure factual and grammatical integrity. The system sets new performance benchmarks on well-known datasets like PubMed and ArXiv.
  arXiv  Detail & Related papers  (2024-08-28T13:52:19Z)
• Exploring Large Language Models for Feature Selection: A Data-centric Perspective [17.99621520553622]
  Large Language Models (LLMs) have influenced various domains, leveraging their exceptional few-shot and zero-shot learning capabilities. We aim to explore and understand the LLMs-based feature selection methods from a data-centric perspective. Our findings emphasize the effectiveness and robustness of text-based feature selection methods and showcase their potentials using a real-world medical application.
  arXiv  Detail & Related papers  (2024-08-21T22:35:19Z)
• Instruction Finetuning for Leaderboard Generation from Empirical AI Research [0.16114012813668935]
  This study demonstrates the application of instruction finetuning of Large Language Models (LLMs) to automate the generation of AI research leaderboards. It aims to streamline the dissemination of advancements in AI research by transitioning from traditional, manual community curation.
  arXiv  Detail & Related papers  (2024-08-19T16:41:07Z)
• Systematic Task Exploration with LLMs: A Study in Citation Text Generation [63.50597360948099]
  Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks. We propose a three-component research framework that consists of systematic input manipulation, reference data, and output measurement. We use this framework to explore citation text generation -- a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric.
  arXiv  Detail & Related papers  (2024-07-04T16:41:08Z)
• TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale [66.01943465390548]
  We introduce TriSum, a framework for distilling large language models' text summarization abilities into a compact, local model. Our method enhances local model performance on various benchmarks. It also improves interpretability by providing insights into the summarization rationale.
  arXiv  Detail & Related papers  (2024-03-15T14:36:38Z)
• Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning [57.74233319453229]
  Large language models (LLMs) have emerged as a groundbreaking technology and their unparalleled text generation capabilities have sparked interest in their application to the fundamental sentence representation learning task. We propose MultiCSR, a multi-level contrastive sentence representation learning framework that decomposes the process of prompting LLMs to generate a corpus. Our experiments reveal that MultiCSR enables a less advanced LLM to surpass the performance of ChatGPT, while applying it to ChatGPT achieves better state-of-the-art results.
  arXiv  Detail & Related papers  (2023-10-17T03:21:43Z)
• Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Models [47.08364281023261]
  Large language models (LLMs) have demonstrated impressive capabilities in natural language generation. However, their output quality can be inconsistent, posing challenges for generating natural language from logical forms (LFs)
  arXiv  Detail & Related papers  (2023-09-21T17:54:58Z)
• Active Learning for Natural Language Generation [17.14395724301382]
  We present a first systematic study of active learning for Natural Language Generation. Our results indicate that the performance of existing AL strategies is inconsistent. We highlight some notable differences between the classification and generation scenarios, and analyze the selection behaviors of existing AL strategies.
  arXiv  Detail & Related papers  (2023-05-24T11:27:53Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [64.22926988297685]
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP) In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
  arXiv  Detail & Related papers  (2019-10-23T17:37:36Z)

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.