Source-linked AI summary
Reframing Instructional Prompts to GPTk's Language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, Hannaneh Hajishirzi
TL;DR
The paper investigates whether complex instructional phrasing contributes to language-model failures and manually reformulates instructions using several reframing techniques. Across 12 NLP tasks and multiple model sizes, reframed prompts outperform raw instructions in few-shot and zero-shot settings and can reduce reliance on costly supervision.
Problem
The paper asks whether low LM performance reflects inherent task difficulty or the complex phrasing of instructional prompts.
Method
The authors manually apply five reframing techniques to instructions for 12 tasks and compare reframed with raw prompts across GPT-family models and prompting settings.
Results
Reframed prompts outperform raw instructions by 14% in few-shot and 17% in zero-shot GPT3-instruct evaluations, with gains remaining consistent across models.
Takeaways & Limitations
Reframing provides model designers with readable prompt guidelines that can improve performance and sample complexity without fine-tuning large models.
Abstract
from arXiv · showhide
What kinds of instructional prompts are easier to follow for Language Models (LMs)? We study this question by conducting extensive empirical analysis that shed light on important features of successful instructional prompts. Specifically, we study several classes of reframing techniques for manual reformulation of prompts into more effective ones. Some examples include decomposing a complex task instruction into multiple simpler tasks or itemizing instructions into sequential steps. Our experiments compare the zero-shot and few-shot performance of LMs prompted with reframed instructions on 12 NLP tasks across 6 categories. Compared with original instructions, our reframed instructions lead to significant improvements across LMs with different sizes. For example, the same reframed prompts boost few-shot performance of GPT3-series and GPT2-series by 12.5% and 6.7% respectively averaged over all tasks. Furthermore, reframed instructions reduce the number of examples required to prompt LMs in the few-shot setting. We hope these empirically-driven techniques will pave the way towards more effective future prompting algorithms.
1 Introduction
The paper asks whether poor LM performance reflects task difficulty or the complex phrasing of instructional prompts. It evaluates manual reframing techniques and finds broad gains over raw instructions across models and settings.
- Non-expert task instructions are often long and abstract, making them difficult for LMs to follow.
- GPT3 succeeds on an entity-coreference question-generation task after the original instruction is decomposed into four simpler sub-steps.
- The study examines five reframing techniques, including low-level patterns, decomposition, itemization, constraints, and specialized instructions.
- 14% and 17% average gains occur with GPT3-instruct in few-shot and zero-shot setups, respectively, versus raw instructions across 12 tasks.
- Reframing improves performance and sample complexity across model sizes, while offering a potential alternative to costly fine-tuning.
2 Related Work
The paper distinguishes manual reframing of complex instructions from prior discrete-prompt and parameter-tuning approaches. It presents reframing as a human-guided alternative that avoids some data and computational costs of training-based methods.
- Prior discrete-prompt work generally makes lightweight changes to original prompts, whereas this paper reframes complex instructions more substantially.
- Meta-training with instructions depends on costly labeled prompts for thousands of tasks and fine-tuning, especially for large LMs.
- Continuous prompt tuning is algorithmic and parameter-efficient but requires gradient propagation across the architecture, creating computational costs for large LMs.
- Manual reframing uses relatively simple rules of thumb and requires human intervention instead of model tuning.
3 Prompt Reframing
The paper derives reframing principles from GPT3 failure modes and operationalizes them as five techniques. These techniques simplify, clarify, constrain, or specialize instructions to address distinct prompting failures.
- 3 Prompt Reframing: Reframing principles are obtained by probing training-set instructions to identify GPT3’s prompting failure modes.
- 3 Prompt Reframing: GPT3 struggles with long, repetitive, abstract prompts that rely on analogies, commonsense, or domain knowledge.
- 3.1 Reframing Principles: Short, concrete statements that avoid background-knowledge terms improve GPT3’s responses to instructions.
- 3.1 Reframing Principles: The five principles use low-level output patterns, bulleted itemization, task decomposition, explicit constraints, and specialized instructions.
- 3.1 Reframing Principles: The techniques range from appending an enforcement statement to completely changing a task through decomposition.
- 3.2 Reframing Techniques: Each technique describes a failure state, proposed approach, and illustrative example based on development-set examples.
- 3.2 Reframing Techniques: Pattern reframing replaces high-level descriptions with low-level output patterns, while itemizing reframing turns requirements into positive bulleted statements.
- 3.2 Reframing Techniques: Decomposition reframing addresses implicit multi-step reasoning by splitting tasks into sequential or parallel subtasks; specialization instead directly states the low-level task.
4 Experimental Setup
The experiments evaluate reframed instructions on 12 NATURAL INSTRUCTIONS tasks spanning six categories, using ROUGE-L and multiple GPT-family models. Comparisons include raw prompting variants, schema selection, calibration, and supervised baselines.
- The evaluation uses 12 tasks from NATURAL INSTRUCTIONS organized into six categories and measures performance with ROUGE-L.
- Models include GPT2, GPT2Large, GPT2XL, GPT3, GPT3-instruct, and BART-base.
- Raw-instruction baselines are tested in few-shot, maximum-example, and zero-shot settings, with five examples in the few-shot setup.
- The study also constructs schema-selection variants by testing 12 instruction encodings and selecting the best-performing encoding for each task.
- Supervised baselines train medium-sized LMs because fine-tuning large models such as GPT3 is prohibitively expensive.
5 Empirical Results
Across the evaluated tasks and categories, reframed instructions generally outperform raw instructions and remain effective across models, while different reframing techniques show task-specific strengths. The analyses also indicate that gains are not explained simply by shorter instructions and that reframing corrects more errors than it introduces.
- Main Results: Reframed instructions consistently outperform raw task instructions across various models.This cross-model advantage contrasts with parameter-tuning methods that must be performed separately for each model.
- Main Results: Reframing in the few-shot setup outperforms supervised baselines by 11% in Answer Generation and 4% in Incorrect Answer Generation.Average supervised-baseline performance remains higher overall, while GPT3-instruct with reframed prompts exceeds the supervised mid-sized GPT2Large model despite its use of 200ˆ more data.
- Reframing Techniques: SPECIALIZATION improves performance across a wider range of tasks, while RESTRAINING, SPECIALIZATION, DECOMPOSITION, and PATTERN reframing show different category-specific strengths.RESTRAINING is strongest for Classification, SPECIALIZATION for Answer Generation, and PATTERN for Question Generation; users are therefore advised to try all five techniques.
- Performance vs Instructions Length: Performance gain is not always proportional to instruction-length reduction, so shortening instructions alone is not necessarily the primary improvement factor.Figure 4 plots length reduction against ROUGE-L gain for tasks evaluated with GPT3-instruct in the few-shot setting.
- Qualitative Analysis: 24% of sampled questions had raw-instruction mistakes corrected by reframing, while reframing caused 4% additional failures.Among corrected errors, ITEMIZING REFRAMING contributed most and RESTRAINING REFRAMING contributed least.
6 Concluding Remarks
The paper introduces five manually applied reframing approaches that make task instructions easier for language models while preserving human readability. It positions the analysis as a preliminary step toward more effective prompting methods, especially where task definitions evolve or fine-tuning is costly.
- Concluding Remarks: The study introduces five approaches that reformulate task instructions to make them easier for language models while maintaining human readability.The approaches are evaluated manually on 12 tasks against raw instructions and fine-tuned mid-sized models.
- Concluding Remarks: Reframing may be particularly useful when task definitions evolve, making crowdsourcing and fine-tuning difficult.Model designers can create new reframed prompts in minutes, according to the authors.
- Concluding Remarks: The authors hope the study will motivate algorithmic few-shot learning methods that generalize across models.They frame reframing as a preliminary stepping stone toward more effective ways to use large language models.
A.1 Examples of Error Types
The appendix points readers to examples illustrating error patterns that reframing resolves over raw instructions. These examples are organized to exemplify the error types summarized in the accompanying analysis.
- Examples of Error Types: Table 5 provides examples of error patterns where model performance improves with reframing over raw instructions.The examples are intended to make the error categories concrete.
A.1.1 Additional Error Analysis:
The additional error analysis separates failures caused by reframing from failures inherited from raw instructions. It reports that retained raw-instruction failures are much more common than reframing-induced failures.
- Additional Error Analysis: 4% of errors are caused by reframing, whereas 31% are raw-instruction failures retained after reframing.Table 6 reports the dominant patterns among these caused and retained failures.
A.2 GPT3-instruct Outputs to Raw and Reframed Instructions
The appendix illustrates how GPT3-instruct responds to raw versus reframed instructions across five reframing techniques. Across diverse tasks, the examples show reframing addressing distinct failure patterns and producing valid task outputs where raw instructions fail.
- A.2 GPT3-instruct Outputs to Raw and Reframed Instructions: The appendix explains reframing techniques through GPT3-instruct error patterns observed in raw and reframed outputs.The examples compare model behavior under alternative instruction framings.
- A.2.1 PATTERN REFRAMING: Pattern reframing helps GPT3 produce valid questions when detailed raw instructions or definition-only reductions fail on CosmosQA and MCTACO.Tables 7–10 provide examples for these question-generation tasks.
- A.2.2 ITEMIZING REFRAMING: Itemizing reframing addresses failures on QASC related-fact generation and miscellaneous tasks by restructuring instructions into itemized forms.Tables 11–13 document tasks requiring itemizing and examples of the reframed instructions.
- A.2.3 DECOMPOSITION REFRAMING: Decomposition reframing helps GPT3 generate valid Winogrande sample questions when both detailed and definition-only raw instructions fail.Tables 14–15 contrast the raw and decomposed instruction examples.
- A.2.4 RESTRAINING REFRAMING: Restraining reframing helps GPT3 produce valid DROP answer types when raw instructions fail and is illustrated across diverse task types.Tables 16–17 show the answer-type example and additional utility examples.
- A.2.5 SPECIALIZATION REFRAMING: Specialization reframing helps overcome GPT3 failures with raw instructions across a diverse set of tasks.Table 18 presents examples of specialization-reframed instructions and corresponding outputs.