What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks
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Updated
Jul 26, 2024 - Jupyter Notebook
What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks
BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks on Large Language Models
Python SDK for experimenting, testing, evaluating & monitoring LLM-powered applications - Parea AI (YC S23)
How good are LLMs at chemistry?
Language Model for Mainframe Modernization
CompBench evaluates the comparative reasoning of multimodal large language models (MLLMs) with 40K image pairs and questions across 8 dimensions of relative comparison: visual attribute, existence, state, emotion, temporality, spatiality, quantity, and quality. CompBench covers diverse visual domains, including animals, fashion, sports, and scenes.
The data and implementation for the experiments in the paper "Flows: Building Blocks of Reasoning and Collaborating AI".
Training and Benchmarking LLMs for Code Preference.
Restore safety in fine-tuned language models through task arithmetic
A minimalist benchmarking tool designed to test the routine-generation capabilities of LLMs.
Develop reliable AI apps
Code and data for Koo et al's ACL 2024 paper "Benchmarking Cognitive Biases in Large Language Models as Evaluators"
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A framework for evaluating the effectiveness of chain-of-thought reasoning in language models.
The MERIT Dataset is a fully synthetic, labeled dataset created for training and benchmarking LLMs on Visually Rich Document Understanding tasks. It is also designed to help detect biases and improve interpretability in LLMs, where we are actively working. This repository is actively maintained, and new features are continuously being added.
Awesome Mixture of Experts (MoE): A Curated List of Mixture of Experts (MoE) and Mixture of Multimodal Experts (MoME)
Benchmark that evaluates LLMs using 436 NYT Connections puzzles
Official repository for "RoMath: A Mathematical Reasoning Benchmark in 🇷🇴 Romanian 🇷🇴"
A platform that enables users to perform private benchmarking of machine learning models. The platform facilitates the evaluation of models based on different trust levels between the model owners and the dataset owners.
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