Summary SCI BENCH Evaluating College-Level Scientific Problem-Solving Abilities arxiv.org
12,017 words - PDF document - View PDF document
One Line
SCI Bench is a benchmark suite that assesses the problem-solving capabilities of large language models by providing comprehensive solutions and discouraging guesswork.
Slides
Slide Presentation (10 slides)
Key Points
- The paper introduces a benchmark suite called SCI BENCH for evaluating the scientific problem-solving abilities of large language models (LLMs).
- GPT-4 with chain-of-thought (CoT) prompting and Python as external tools have errors in calculation and misunderstanding mathematical equations.
- The SCI BENCH evaluation focuses on two representative LLMs, GPT-3.5 and GPT-4, and their performance in scientific problem-solving.
- LLMs have limitations in solving complex reasoning tasks, but external tools like Toolformer and Chameleon have been proposed to enhance their capabilities.
- GPT-4 outperforms GPT-3.5 in all experimental settings, with notable improvements in few-shot learning with CoT prompting and Python as external tools.
- Causal reasoning, problem deduction skills, and abstract reasoning are important abilities in college-level scientific problem-solving.
- The error rate of models can be reduced when the system prompt specifies the scientific domain.
- The text excerpt includes various equations and solutions to scientific problems, including examples of syntax errors in the solutions.
Summaries
31 word summary
SCI BENCH is a benchmark suite that evaluates the problem-solving abilities of large language models (LLMs). It overcomes existing benchmark limitations by offering detailed solutions and preventing LLMs from guessing answers.
41 word summary
The paper introduces SCI BENCH, a benchmark suite designed to evaluate the scientific problem-solving abilities of large language models (LLMs). It aims to address the limitations of existing benchmarks by providing detailed solutions and preventing LLMs from guessing answers. The evaluation
617 word summary
The paper introduces a benchmark suite called SCI BENCH, which aims to evaluate the scientific problem-solving abilities of large language models (LLMs). This benchmark includes collegiate-level scientific problems from various subjects and undergraduate-level exams. The study conducted using this benchmark reveals
GPT-4 with chain-of-thought (CoT) prompting and Python as external tools both have errors in calculation and misunderstanding mathematical equations. Existing benchmarks lack detailed solutions and allow LLMs to guess answers from multiple-choice questions, potentially misleading evaluation
The SCI BENCH evaluation focuses on two representative large language models (LLMs), GPT-3.5 and GPT-4, and their performance in scientific problem-solving. The evaluation includes various prompting strategies and the use of external tools. The
This selection process aims to prevent information leakage from pre-existing question banks. The evaluation of LLMs focuses on advanced computational abilities, including performing complex mathematical computations. Ten textbooks from physics, chemistry, and math are selected as the open textbook dataset. The
We collected 695 problems from textbooks, 112 of which have detailed step-by-step solutions. The close exam dataset includes 104 problems from real-world midterms and final exams. The textbook dataset consists of problems with single numeric answers and separate units,
LLMs have limitations in solving complex reasoning tasks, so external tools like Toolformer and Chameleon have been proposed to enhance their capabilities. In line with this approach, the model in this study is prompted to convert its solution steps into Wolfram Language or
GPT-4 outperforms GPT-3.5 in all experimental settings, with notable improvements in few-shot learning with CoT prompting and Python as external tools. Few-shot learning performs better than zero-shot learning in specialized domains like quantum chemistry
Causal reasoning, problem deduction skills, and abstract reasoning are important abilities in college-level scientific problem-solving. The performance of language models (LLMs) in these skills is evaluated using a self-critique protocol. The LLMs lack specific problem
When a system prompt specifies the scientific domain, the error rate of models can be reduced from 11.6% to 5.4%. Traditional benchmarks evaluate general model abilities, while recent benchmarks focus on scientific and mathematical problem-solving skills. However,
This text excerpt consists of a list of references to various papers and books. The references include authors, titles, publication years, and page numbers. The main theme of these references seems to be language models, large language models trained on code, and their
This text excerpt is a list of references to various research papers related to evaluating college-level scientific problem-solving abilities. The references include papers on topics such as multitask language understanding, mathematical problem solving, probability and statistical inference, large language models, machine reading
This document contains a list of references cited in the paper "SCI BENCH: Evaluating College-Level Scientific Problem-Solving Abilities." The references include papers and technical reports related to topics such as language models, mathematical reasoning, deep learning, and quantum chemistry
Chain of thought prompting is used to elicit reasoning in large language models. The document provides examples of problem-solving abilities in current language models (LLMs) and evaluates their performance. One example problem involves calculating the de Broglie wavelength of an electron
The text excerpt includes various equations and solutions to scientific problems. In the first part, a problem using Simpson's Rule to approximate an integral is solved, but the solution contains a syntax error. Another problem involving the equations for c1 and c2 is
Halley's comet, with an eccentricity of 0.967 and a period of 76 years, is calculated to have a minimum distance from the Sun of 8.8 x 10^10 m. The probability that the measured value
Spatial perception, causal reasoning, problem deduction skills, abstract reasoning, scientific literacy, code conversion skills, logical reasoning, and calculation skills are all important abilities for college-level scientific problem-solving. The training prompts for zero-shot chain-of-thought and few-shot