Explainpaper is a service that uses language models to explain user uploaded papers. In my experience the language models are impressively good and the interface is minimalist and easy to use.
These are some demonstration explanations generated by Explainpaper (the questions are written by me, quotes are from the original paper):
Large Language Models Are Human-Level Prompt Engineers
By conditioning on natural language instructions, large language models (LLMs) have
displayed impressive capabilities as general-purpose computers. However, task performance
depends significantly on the quality of the prompt used to steer the model, and most effective
prompts have been handcrafted by humans. Inspired by classical program synthesis and the
human approach to prompt engineering, we propose Automatic Prompt Engineer (APE) for
automatic instruction generation and selection. In our method, we treat the instruction as
the āprogram,ā optimized by searching over a pool of instruction candidates proposed by an
LLM in order to maximize a chosen score function. To evaluate the quality of the selected
instruction, we evaluate the zero-shot performance of another LLM following the selected
instruction. Experiments on 24 NLP tasks show that our automatically generated instructions
outperform the prior LLM baseline by a large margin and achieve better or comparable
performance to the instructions generated by human annotators on 19/24 tasks. We conduct
extensive qualitative and quantitative analyses to explore the performance of APE. We
show that APE-engineered prompts can be applied to steer models toward truthfulness
and/or informativeness, as well as to improve few-shot learning performance by simply
prepending them to standard in-context learning prompts. Please check out our webpage at
APE
ExplainPaper:
The text is discussing a method of automatically generating instructions for a task, to be used by a large language model. The method is designed to improve task performance by optimizing a chosen score function. The text claims that the automatically generated instructions outperform a prior LLM baseline by a large margin.
In our method, we treat the instruction as
the āprogram,ā optimized by searching over a pool of instruction candidates proposed by an
LLM in order to maximize a chosen score function. To evaluate the quality of the selected
instruction, we evaluate the zero-shot performance of another LLM following the selected
instruction.
ExplainPaper:
Our method involves treating the instruction as the āprogramā and using an LLM to select the best instruction from a pool of candidates. We then evaluate the zero-shot performance of another LLM following the selected instruction to see how effective it is.
Inspired by classical program synthesis and the
human approach to prompt engineering, we propose Automatic Prompt Engineer (APE) for
automatic instruction generation and selection. In our method, we treat the instruction as
the āprogram,ā optimized by searching over a pool of instruction candidates proposed by an
LLM in order to maximize a chosen score function. To evaluate the quality of the selected
instruction, we evaluate the zero-shot performance of another LLM following the selected
instruction.
This text is discussing the Automatic Prompt Engineer, or APE, which is a method for automatically generating and selecting instructions. The APE treats the instruction as a āprogramā that is optimized by searching over a pool of instruction candidates. The quality of the selected instruction is evaluated by the zero-shot performance of another LLM following the selected instruction.
P.J.:
How is the āzero-shot performance of another LLMā calculated?
ExplainPaper:
The zero-shot performance of another LLM is calculated by how well the LLM can follow the selected instruction without any prior knowledge of the task.
First, we use an LLM as an inference
model (Ellis et al., 2021; Honovich et al., 2022) to generate instruction candidates based on a small set
of demonstrations in the form of input-output pairs. Next, we guide the search process by computing
a score for each instruction under the LLM we seek to control. Finally, we propose an iterative Monte
Carlo search method where LLMs improve the best candidates by proposing semantically similar
instruction variants. Intuitively, our algorithm asks LLMs to generate a set of instruction candidates
based on demonstrations and then asks them to assess which instructions are more promising.
ExplainPaper:
First, the researchers use a model (Ellis et al., 2021; Honovich et al., 2022) to generate possible instructions based on a small set of examples of input and output pairs. Next, they use a scoring system to determine which of these instructions is most likely to be successful. Finally, they propose an iterative search method where the model improves the best candidates by proposing similar instructions.
P.J.:
If they are generating large numbers of unique prompts and responses could they generate a dataset of this text; what issues might arise if they pursued this approach?
If the researchers are generating a large number of unique prompts and responses, they could create a dataset of this text. However, there could be some issues if they pursued this approach. For example, the dataset might not be representative of all possible prompts and responses, or it might be difficult to label all the data correctly.
Our proposed method, APE, achieves human-level performance on zero-shot learning with
model-generated instructions on 19/24 NLP tasks.
ExplainPaper:
Our proposed method, APE, achieves human-level performance on zero-shot learning with model-generated instructions. This means that it can learn new tasks without any training data, just by being given a description of the task.
P.J.:
What type of tasks are Humans better at generating prompts for?
ExplainPaper:
Our proposed method, APE, achieves human-level performance on zero-shot learning with model-generated instructions. This means that it can learn new tasks without any training data, just by being given a description of the task.
Large Language Models Scaling up transformer-based language models in terms of model size,
training data, and training compute has been shown to predictably improve performance on a wide
range of downstream NLP tasks (Vaswani et al., 2017; Devlin et al., 2018; Brown et al., 2020).
Many emergent abilities (Wei et al., 2022a) of LLMs have been discovered as a result of this scaling,
including few-shot in-context learning, zero-shot problem solving, chain of thought reasoning,
instruction following, and instruction induction (Cobbe et al., 2021; Wei et al., 2022b; Kojima et al.,
2022; Sanh et al., 2022; Wei et al., 2021; Ouyang et al., 2022; Honovich et al., 2022). In this paper, we
view LLMs as black-box computers that execute programs specified by natural language instructions
and investigate how to control an LLMās behavior using model-generated instructions.
ExplainPaper:
The text is discussing how large language models (LLMs) can be used to improve performance on a variety of tasks. It mentions that many abilities of LLMs have been discovered as a result of scaling up the models, including the ability to learn from few examples, solve problems without any training data, and reason through a chain of thoughts. The text then goes on to say that in this paper, the authors are investigating how to control an LLMās behavior using instructions that are generated by the model itself.
Prompt Engineering Prompting offers a natural and intuitive interface for humans to interact withand use generalist models such as LLMs. Due to its flexibility, prompting has been widely used as a generic method for NLP tasks (Schick & SchĆ¼tze, 2021; Brown et al., 2020; Sanh et al., 2022). However, LLMs require careful prompt engineering, either manually (Reynolds & McDonell, 2021) or automatically (Gao et al., 2021; Shin et al., 2020), as models do not seem to understand the prompts in the same way a human would (Webson & Pavlick, 2021; Lu et al., 2021). Though many successful prompt tuning methods perform optimization over a continuous space using gradient-based methods (Liu et al., 2021; Qin & Eisner, 2021; Lester et al., 2021), this becomes less practical with scale, as computing computing gradients becomes increasingly expensive and access to models shifts to APIs that may not provide gradient access. In our paper, we borrow components from discrete prompt search methods, such as prompt generation (Gao et al., 2021; Ben-David et al., 2021), prompt scoring (Davison et al., 2019) and prompt paraphrasing (Jiang et al., 2020; Yuan et al., 2021) to optimize instructions by searching directly in the natural language hypothesis space. As compared to this past work, which uses specialized models for each component and leans heavily on human templates, we show that the entire search can be conducted by a single LLM.
ExplainPaper:
The text explains a method for automatically generating instructions for a task, using a large language model. The method involves generating several instruction candidates, either via direct inference or a recursive process based on semantic similarity, executing them using the target model, and selecting the most appropriate instruction based on computed evaluation scores.
P.J.:
Is it necessary to filter the prompts? How well would language model prompt generation work if you generated a single prompt based on examples?
ExplainPaper:
No, it is not necessary to filter the prompts. Language model prompt generation can work well if you generate a single prompt based on examples.
We consider a task specified by a dataset Dtrain = {(Q, A)} of input/output demonstrations sampled from population X , and a prompted model M. The goal of natural language program synthesis is to find a single instruction Ļ such that, when M is prompted with the concatenation [Ļ; Q] of instruction and a given input, M produces the corresponding output A. More formally, we frame this as an optimization problem, where we seek instruction Ļ that maximizes the expectation of some per-sample score f (Ļ, Q, A) over possible (Q, A):
ExplainPaper:
The goal of natural language program synthesis is to find a single instruction Ļ, such that when the prompted model M is given the instruction and a input Q, it produces the corresponding output A. This is formalized as an optimization problem, where we seek an instruction Ļ that will maximize the expectation of some per-sample score f(Ļ, Q, A) over all possible (Q, A) pairs.
P.J.:
That is a great explanation thanks! It is easier to understand than the original.
ExplainPaper:
Youāre welcome!
We assess the effectiveness of zero-shot and few-shot in-context learning on 24 instruction induction tasks proposed in Honovich et al. (2022). The tasks span many facets of language understanding, from simple phrase structure to similarity and causality identification. We refer the reader to Appendix A of Honovich et al. (2022) for detailed descriptions of each task. For each task, we sample five input-output pairs from the training data and select the best instruction using algorithm 1. Then, we evaluate the quality of the instruction by executing the instruction on InstructGPT 2. We repeat our experiments five times with different random seeds to report the mean and standard deviation of the best performing result in each seed and report the best overall performance in Appendix (Figure 13 and 14). The exact templates for our experiments can be found in Appendix (Table 2).
ExplainPaper:
The text is discussing the effectiveness of zero-shot and few-shot in-context learning on 24 instruction induction tasks. The tasks cover many different aspects of language understanding, from simple phrases to more complicated concepts like causality and similarity. The researchers ran experiments to test how well the instruction-learning algorithm worked on these tasks, and they report the results in Appendix A of the paper.
P.J.:
Is this an good methodology?
ExplainPaper:
The methodology used in this research paper seems to be effective in testing the instruction-learning algorithm on a variety of tasks. The results are reported in detail in Appendix A, which allows readers to see how well the algorithm performed on each task.
LLMs can be seen as general-purpose computers that execute programs specified by natural language prompts. We automate the prompt engineering process by formulating it as a black-box optimization problem, which we propose to solve using efficient search algorithms guided by LLMs. Our method achieves human-level performance on various tasks with minimum human inputs. As recent LLMs demonstrate an impressive ability to follow human instruction, we expect many future models, including those for formal program synthesis, to have a natural language interface. This work builds the foundation to control and steer generative AIs.
ExplainPaper:
The text is discussing how LLMs (which stands for language models) can be used to create programs by using natural language prompts. The author is saying that their method of creating these programs is efficient and achieves good results.
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