(12)Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard评估ChatGPT、GPT-4和Bard的数学和推理能力(13)Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation From Deductive, Inductive ...
22.3 Table2NL 22.4 TableQA 22.5 LLM在表格数据上的能力 22.6 结构化数据/符号数据 本次对论文分类进行优化,目前大体是按照元研究/high-level层面、数据、模型、算法的顺序来的,像是知识、幻觉是归类到模型本身的能力中的,代码和表格算是单独开一个领域,放在后面,分类还是比较多,是因为不想分第三级标题除了顺序的...
[10] https://bigscience.notion.site/BLOOM-BigScience-176B-Model-ad073ca07cdf479398d5f95d88e218c4 [11] https://deepchecks.com/llm-models-comparison/
from pydantic import BaseModel from typing import List from llama_index.program import OpenAIPydanticProgram # Define output schema (without docstring) class Song(BaseModel): title: str length_seconds: int class Album(BaseModel): name: str artist: str songs: List[Song] # Define openai pydantic ...
记录输入提示和模型输出至关重要。MLflow 使用以下方式将它们存储为 CSV 格式的工件：mlflow.log_table(...
The highest scores for each model are highlighted in bold, and the second highest with underline. Zephyr-7B Llama2-Chat-13B GPT-4 ROUGE-L 0.240 0.244 0.279 BLEURT 0.397 0.396 0.411 BERTScore 0.582 0.585 0.593 MoverScore 0.300 0.301 0.310 Table 5. T-test comparison of LLMs showing ...
With a fixed $100K budget, we focus on 100B+ parameters. Although the Chinchilla laws [19] suggest that training a smaller model with more data may potentially result in higher scores on some benchmarks due to more sufficient training, we believe that verifying the feasibility of a growth ...
Full size table In summary, the workflow, described in detail below using module M9.2 as an illustrative example, was as follows: Step1: Selecting one of the A37 modules. Step 2: Identifying functional convergences among the pool of candidate genes. Step 3: Scoring each candidate gene across...
Table 3. Latency comparison on GSM8K. LLMLingua can accelerate LLMs’ end-to-end inference by a factor of 1.7–5.7x. Table 4. Recovering the compressed prompt from GSM8K using GPT-4. Enhancing the user experience and looking ahead
The combination of these diverse methods has led to no-table advancements, resulting in enhanced retrieval outcomes and improved performance for RAG. 这些不同方法的组合导致了无表的进步,从而增强了检索结果并改进了RAG的性能。 Fine-tuning Embedding Models Once the appropriate size of chunks is determined...