From 6610ba54ad8da2dc0fca19c1cb8f09e68d45cea4 Mon Sep 17 00:00:00 2001 From: Per Harald Borgen Date: Wed, 27 Sep 2023 19:46:57 +0200 Subject: [PATCH] Fix typo: Adding a missing "if" in sentence about ReAct in related_resources.md (#737) --- related_resources.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/related_resources.md b/related_resources.md index 22f4d819..8b78ccbc 100644 --- a/related_resources.md +++ b/related_resources.md @@ -46,7 +46,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a - [Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling (2023)](https://arxiv.org/abs/2305.09993): Automated searching over possible chain-of-thought prompts improved ChatGPT's scores on a few benchmarks by 0–20 percentage points. - [Faithful Reasoning Using Large Language Models (2022)](https://arxiv.org/abs/2208.14271): Reasoning can be improved by a system that combines: chains of thought generated by alternative selection and inference prompts, a halter model that chooses when to halt selection-inference loops, a value function to search over multiple reasoning paths, and sentence labels that help avoid hallucination. - [STaR: Bootstrapping Reasoning With Reasoning (2022)](https://arxiv.org/abs/2203.14465): Chain of thought reasoning can be baked into models via fine-tuning. For tasks with an answer key, example chains of thoughts can be generated by language models. -- [ReAct: Synergizing Reasoning and Acting in Language Models (2023)](https://arxiv.org/abs/2210.03629): For tasks with tools or an environment, chain of thought works better you prescriptively alternate between **Re**asoning steps (thinking about what to do) and **Act**ing (getting information from a tool or environment). +- [ReAct: Synergizing Reasoning and Acting in Language Models (2023)](https://arxiv.org/abs/2210.03629): For tasks with tools or an environment, chain of thought works better if you prescriptively alternate between **Re**asoning steps (thinking about what to do) and **Act**ing (getting information from a tool or environment). - [Reflexion: an autonomous agent with dynamic memory and self-reflection (2023)](https://arxiv.org/abs/2303.11366): Retrying tasks with memory of prior failures improves subsequent performance. - [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (2023)](https://arxiv.org/abs/2212.14024): Models augmented with knowledge via a "retrieve-then-read" can be improved with multi-hop chains of searches. - [Improving Factuality and Reasoning in Language Models through Multiagent Debate (2023)](https://arxiv.org/abs/2305.14325): Generating debates between a few ChatGPT agents over a few rounds improves scores on various benchmarks. Math word problem scores rise from 77% to 85%.