Prompting Large Language Models for Clinical Temporal Relation Extraction

• URL: http://arxiv.org/abs/2412.04512v1
• Date: Wed, 04 Dec 2024 18:35:28 GMT
• Title: Prompting Large Language Models for Clinical Temporal Relation Extraction
• Authors: Jianping He, Laila Rasmy, Haifang Li, Jianfu Li, Zenan Sun, Evan Yu, Degui Zhi, Cui Tao,
• Abstract summary: This study utilizes four large language models (LLMs) for clinical temporal relation extraction (CTRE)<n>We developed full (FFT) and parameter-efficient (PEFT) fine-tuning strategies and evaluated these strategies on the 2012 i2b2 CTRE task.
• Score: 5.403858596195122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective: This paper aims to prompt large language models (LLMs) for clinical temporal relation extraction (CTRE) in both few-shot and fully supervised settings. Materials and Methods: This study utilizes four LLMs: Encoder-based GatorTron-Base (345M)/Large (8.9B); Decoder-based LLaMA3-8B/MeLLaMA-13B. We developed full (FFT) and parameter-efficient (PEFT) fine-tuning strategies and evaluated these strategies on the 2012 i2b2 CTRE task. We explored four fine-tuning strategies for GatorTron-Base: (1) Standard Fine-Tuning, (2) Hard-Prompting with Unfrozen LLMs, (3) Soft-Prompting with Frozen LLMs, and (4) Low-Rank Adaptation (LoRA) with Frozen LLMs. For GatorTron-Large, we assessed two PEFT strategies-Soft-Prompting and LoRA with Frozen LLMs-leveraging Quantization techniques. Additionally, LLaMA3-8B and MeLLaMA-13B employed two PEFT strategies: LoRA strategy with Quantization (QLoRA) applied to Frozen LLMs using instruction tuning and standard fine-tuning. Results: Under fully supervised settings, Hard-Prompting with Unfrozen GatorTron-Base achieved the highest F1 score (89.54%), surpassing the SOTA model (85.70%) by 3.74%. Additionally, two variants of QLoRA adapted to GatorTron-Large and Standard Fine-Tuning of GatorTron-Base exceeded the SOTA model by 2.36%, 1.88%, and 0.25%, respectively. Decoder-based models with frozen parameters outperformed their Encoder-based counterparts in this setting; however, the trend reversed in few-shot scenarios. Discussions and Conclusions: This study presented new methods that significantly improved CTRE performance, benefiting downstream tasks reliant on CTRE systems. The findings underscore the importance of selecting appropriate models and fine-tuning strategies based on task requirements and data availability. Future work will explore larger models and broader CTRE applications.

Related papers

• GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models [194.64264251080454]
  We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters.<n>Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning, GLM-4.5 achieves strong performance across agentic, reasoning, and coding tasks.<n>We release both GLM-4.5 (355B parameters) and a compact version, GLM-4.5-Air (106B parameters), to advance research in reasoning and agentic AI systems.
  arXiv  Detail & Related papers  (2025-08-08T17:21:06Z)
• EfficientLLM: Efficiency in Large Language Models [64.3537131208038]
  Large Language Models (LLMs) have driven significant progress, yet their growing counts and context windows incur prohibitive compute, energy, and monetary costs.<n>We introduce EfficientLLM, a novel benchmark and the first comprehensive empirical study evaluating efficiency techniques for LLMs at scale.
  arXiv  Detail & Related papers  (2025-05-20T02:27:08Z)
• Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMs [11.1036247482657]
  We present SMETimes, the first systematic investigation of sub-3B parameter SLMs for efficient and accurate time series forecasting. Our approach centers on three key innovations: A statistically-enhanced prompting mechanism that bridges numerical time series with textual semantics through statistical features; A adaptive fusion embedding architecture that aligns temporal patterns with language model token spaces through learnable parameters.
  arXiv  Detail & Related papers  (2025-03-05T15:27:36Z)
• SECURA: Sigmoid-Enhanced CUR Decomposition with Uninterrupted Retention and Low-Rank Adaptation in Large Language Models [9.090588805667263]
  We propose SECURA: Sigmoid-Enhanced CUR Decomposition LoRA, a novel PEFT variant designed to mitigate catastrophic forgetting. Our method introduces a novel normalization technique, Sigmoid-based Magnitude Norm (S-MagNorm), which enhances parameter retention and fine-tuning efficiency.
  arXiv  Detail & Related papers  (2025-02-25T13:00:05Z)
• DeltaLLM: Compress LLMs with Low-Rank Deltas between Shared Weights [11.047879241587315]
  We introduce DeltaLLM, a new post-training compression technique to reduce the memory footprint of LLMs. For training, we adopt the progressing module replacement method and show that the lightweight training of the low-rank modules is sufficient to achieve performance on par with LLMs of comparable sizes trained from scratch. Our method also outperforms compression techniques JointDrop, LaCo, ShortGPT and SliceGPT with the same number of parameters removed.
  arXiv  Detail & Related papers  (2025-01-30T18:59:55Z)
• LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization [78.93425154518705]
  Low-rank adaption (LoRA) is a widely used parameter-efficient finetuning method for LLM that reduces memory requirements. This paper introduces LoRA-RITE, a novel adaptive matrix preconditioning method for LoRA optimization.
  arXiv  Detail & Related papers  (2024-10-27T22:57:12Z)
• LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints [86.59857711385833]
  We introduce RealInstruct, the first benchmark designed to evaluate LLMs' ability to follow real-world multi-constrained instructions. To address the performance gap between open-source and proprietary models, we propose the Decompose, Critique and Refine (DeCRIM) self-correction pipeline. Our results show that DeCRIM improves Mistral's performance by 7.3% on RealInstruct and 8.0% on IFEval even with weak feedback.
  arXiv  Detail & Related papers  (2024-10-09T01:25:10Z)
• DLP-LoRA: Efficient Task-Specific LoRA Fusion with a Dynamic, Lightweight Plugin for Large Language Models [10.179598253424103]
  Large Language Models (LLMs) have achieved robust performance across diverse tasks, but fine-tuning these models for specific domains remains resource-intensive. We propose a mini-MLP module with only 5M parameters to dynamically fuse multiple LoRAs at the sentence level using top-p sampling strategies. This approach reduces inference time to less than twice that of single LoRA inference by leveraging parallel computation.
  arXiv  Detail & Related papers  (2024-10-02T12:45:52Z)
• LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement [79.31084387589968]
  Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. We propose LLM2LLM, a data augmentation strategy that uses a teacher LLM to enhance a small seed dataset. We achieve improvements up to 24.2% on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC and 39.8% on SST-2 over regular fine-tuning in the low-data regime.
  arXiv  Detail & Related papers  (2024-03-22T08:57:07Z)
• Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning [24.050346319335098]
  Methods based on the fine-tuning strategy have limited transfer learning ability for cross-domain applications. This study proposed a novel approach that employs a soft prompt-based learning architecture, which introduces trainable prompts to guide LLMs toward desired outputs.
  arXiv  Detail & Related papers  (2024-03-19T02:34:33Z)
• How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments [83.78240828340681]
  GAMA($gamma$)-Bench is a new framework for evaluating Large Language Models' Gaming Ability in Multi-Agent environments. $gamma$-Bench includes eight classical game theory scenarios and a dynamic scoring scheme specially designed to assess LLMs' performance. Our results indicate GPT-3.5 demonstrates strong robustness but limited generalizability, which can be enhanced using methods like Chain-of-Thought.
  arXiv  Detail & Related papers  (2024-03-18T14:04:47Z)
• Enabling Weak LLMs to Judge Response Reliability via Meta Ranking [38.63721941742435]
  We propose a novel cross-query-comparison-based method called $textitMeta Ranking$ (MR) MR assesses reliability by pairwisely ranking the target query-response pair with multiple reference query-response pairs. We show that MR can enhance strong LLMs' performance in two practical applications: model cascading and instruction tuning.
  arXiv  Detail & Related papers  (2024-02-19T13:57:55Z)
• Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning [52.29522018586365]
  We study structured pruning as an effective means to develop smaller LLMs from pre-trained, larger models. Our approach employs two key techniques: (1) targeted structured pruning, which prunes a larger model to a specified target shape by removing layers, heads, and intermediate and hidden dimensions in an end-to-end manner, and (2) dynamic batch loading, which dynamically updates the composition of sampled data in each training batch based on varying losses across different domains.
  arXiv  Detail & Related papers  (2023-10-10T15:13:30Z)
• Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction [26.504643007899592]
  We develop soft prompt-based learning algorithms for large language models (LLMs) We compare 4 training strategies including fine-tuning without prompts, hard-prompt with unfrozen LLMs, soft-prompt with frozen LLMs, and soft-prompt with frozen LLMs. We evaluate the transfer learning ability of the prompt-based learning algorithms in a cross-institution setting.
  arXiv  Detail & Related papers  (2023-10-10T01:27:08Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

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.