Papers with 0-shot setting
The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding? (2025.findings-acl)
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| Challenge: | Existing approaches to self-improvement rely on external supervision signals in the form of seed data and/or assistance from third-party models. |
| Approach: | They propose a framework for generating high-quality synthetic question-answer data in a fully autonomous manner. |
| Outcome: | The proposed framework generates high-quality synthetic question-answer data in a fully autonomous manner. |
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. |
| Approach: | They propose to make Large Language Models (LLMs) operating in 0-shot or few-shot settings as efficient as 0- shot text classifiers by leveraging a small number of samples. |
| Outcome: | The proposed model is able to perform better on multiple datasets than existing models on 0-shot or few-shot settings. |
Ranking LLM-Generated Loop Invariants for Program Verification (2023.findings-emnlp)
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Saikat Chakraborty, Shuvendu Lahiri, Sarah Fakhoury, Akash Lal, Madanlal Musuvathi, Aseem Rastogi, Aditya Senthilnathan, Rahul Sharma, Nikhil Swamy
| Challenge: | Large Language Models (LLMs) are capable of synthesizing inductive loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariant. |
| Approach: | They propose a re-ranking approach to generate inductive loop invariants using Large Language Models . they propose reranking rankers that can distinguish between correct and incorrect attempts . |
| Outcome: | The proposed method reduces the number of calls to a verifier by comparing the generated results with the original model. |