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Keyword: chain-of-thought prompting ×Clear all

11 claims from 7 papers

1. Computer Science › Topic Modeling

   Self-Consistency Improves Chain of Thought Reasoning in Language Models

   Wang, Jason, Schuurmans et al. · arXiv (Cornell University) · 2022

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   1. UncheckedThe paper reports that self-consistency improves chain-of-thought prompting on arithmetic and commonsense reasoning benchmarks, by 3.9 to 17.9 percentage points.“Our extensive empirical evaluation shows that self-consistency boosts the performance of chain-of-thought prompting with a striking margin on a range of popular arithmetic and commonsense reasoning benchmarks, including GSM8K (+17.9%), SVAMP (+11.0%), AQuA (+12.2%), StrategyQA (+6.4%) and ARC-chall…”

2. Computer Science › Topic Modeling

   Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

   Süzgün, Nathan, Schärli et al. · arXiv (Cornell University) · 2022

   The authors pick 23 BIG-Bench tasks where language models had not beaten average human raters, and test whether chain-of-thought prompting lets models do better on them.

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   1. UncheckedWith chain-of-thought prompting, PaLM beat the average human rater on 10 of 23 hard BIG-Bench tasks, and Codex (code-davinci-002) on 17 of 23.“We find that applying chain-of-thought (CoT) prompting to BBH tasks enables PaLM to surpass the average human-rater performance on 10 of the 23 tasks, and Codex (code-davinci-002) to surpass the average human-rater performance on 17 of the 23 tasks.”

3. Engineering › Robotic Process Automation Applications

   Artificial Intelligence Co-Piloted Auditing

   Gu, Schreyer, Moffitt and Vasarhelyi · SSRN Electronic Journal · 2023

   The paper proposes auditors working alongside foundation models such as GPT-4, and illustrates this with prompt-based adaptation of ChatGPT on three audit tasks.

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   1. UncheckedThe authors say they show co-piloted auditing is promising by adapting GPT-4 through ChatGPT for three audit tasks: ratio analysis, text mining and journal entry testing.“We demonstrate the potential of co-piloted auditing, by fine-tuning GPT-4 using OpenAI's ChatGPT interface towards three different audit tasks namely financial ratio analysis, text mining, and journal entry testing.”

4. Computer Science › AI-based Problem Solving and Planning

   Reasoning with Language Model is Planning with World Model

   Hao, Gu, Ma et al. · arXiv (Cornell University) · 2023

   The paper proposes RAP, which has a language model act as both world model and reasoning agent with tree search, and reports it beating strong prompting baselines on planning, maths and logic tasks.

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   1. UncheckedIn a plan generation setting, the RAP method on LLAMA-33B is reported to beat chain-of-thought prompting on GPT-4, by 33% relative.“RAP on LLAMA-33B surpasses CoT on GPT-4 with 33% relative improvement in a plan generation setting.”

5. Computer Science › Topic Modeling

   TheoremQA: A Theorem-driven Question Answering dataset

   Chen, Yin, Ku et al. · arXiv (Cornell University) · 2023

   The authors built TheoremQA, an 800-question dataset testing whether AI models can apply theorems to hard science problems, and used it to evaluate 16 language and code models.

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   1. UncheckedOn the TheoremQA benchmark, GPT-4 scored 51% with Program-of-Thoughts prompting, a result the authors describe as unparalleled among the models tested.“We found that GPT-4's capabilities to solve these problems are unparalleled, achieving an accuracy of 51% with Program-of-Thoughts Prompting.”
   2. UncheckedOn TheoremQA, every existing open-source model tested scored below 15% accuracy, which the authors say is barely above a random-guess baseline.“All the existing open-sourced models are below 15%, barely surpassing the random-guess baseline.”

6. Computer Science › Topic Modeling

   On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning

   Shaikh, Zhang, William, Bernstein and Yang · arXiv (Cornell University) · 2022

   The authors tested zero-shot chain-of-thought prompting on harmful questions and stereotype benchmarks, and report that it makes harmful or undesirable output more likely across prompt formats and model variants.

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   1. Unchecked“We find that zero-shot CoT reasoning in sensitive domains significantly increases a model's likelihood to produce harmful or undesirable output, with trends holding across different prompt formats and model variants.”
   2. UncheckedIn this study, harmful chain-of-thought outputs rose with model size but fell as models got better at following instructions.“Furthermore, we show that harmful CoTs increase with model size, but decrease with improved instruction following.”

7. Computer Science › Topic Modeling

   Inverse scaling can become U-shaped

   Jason, Najoung, Tay and Le · arXiv (Cornell University) · 2022

   The paper re-tests eleven tasks where bigger language models did worse, using models up to 540B parameters, and finds most no longer show worsening performance at the largest sizes.

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   1. Unchecked“With this increased range of model sizes and training compute, only four out of the eleven tasks remain inverse scaling.”
   2. UncheckedThe paper finds that giving a language model one worked example, or prompting it to reason step by step, can further reduce worsening performance as models grow.“In addition, we find that 1-shot examples and chain-of-thought can help mitigate undesirable scaling patterns even further.”
   3. UncheckedOf eleven tasks where bigger models did worse, six showed U-shaped scaling: performance fell, then rose again at the largest size tested; one improved throughout.“Six out of the eleven tasks exhibit "U-shaped scaling", where performance decreases up to a certain size, and then increases again up to the largest model evaluated (the one remaining task displays positive scaling).”