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Found 10 papers, 5 papers with code
OrdRankBen: A Novel Ranking Benchmark for Ordinal Relevance in NLP
The evaluation of ranking tasks remains a significant challenge in natural language processing (NLP), particularly due to the lack of direct labels for results in real-world scenarios.
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading
Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks.
Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance
While large language models (LLMs) have shown strong general reasoning capabilities, their effectiveness in financial reasoning, which is crucial for real-world financial applications remains underexplored.
INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent
Despite this progress, the field currently encounters two main challenges: (1) the lack of a comprehensive LLM agent framework adaptable to a variety of financial tasks, and (2) the absence of standardized benchmarks and consistent datasets for assessing agent performance.
Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?
On unseen i2b2 data, LLaMA-3-70B outperformed BERT by 7% (F1) on NER and 4% on RE.
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Financial LLMs hold promise for advancing financial tasks and domain-specific applications.
Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations
This study underscores the effectiveness of LLMs like GPT in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice.
Me LLaMA: Foundation Large Language Models for Medical Applications
This work underscores the importance of domain-specific data in developing medical LLMs and addresses the high computational costs involved in training, highlighting a balance between pre-training and fine-tuning strategies.
A systematic evaluation of large language models for biomedical natural language processing: benchmarks, baselines, and recommendations
The biomedical literature is rapidly expanding, posing a significant challenge for manual curation and knowledge discovery.
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
Results: Using baseline prompts, GPT-3. 5 and GPT-4 achieved relaxed F1 scores of 0. 634, 0. 804 for MTSamples, and 0. 301, 0. 593 for VAERS.
