Papers with zero-shot chain-of-thought
Improving LLM Reasoning through Interpretable Role-Playing Steering (2025.findings-emnlp)
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| Challenge: | Existing methods for role-playing rely on prompt engineering, which lacks stability and interpretability. |
| Approach: | They propose a framework that extracts latent representations from role-play prompts and constructs a steering vector that can be injected into the model's residual stream with controllable intensity. |
| Outcome: | The proposed framework extracts latent representations from role-play prompts, selects the most relevant features based on activation patterns, and constructs a steering vector that can be injected into the model’s residual stream with controllable intensity. |
AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought (2024.findings-acl)
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| Challenge: | Existing approaches to cross-lingual chain-of-thought integrate reasoning knowledge from different languages, but they still rely on manual language specification and weight allocation. |
| Approach: | They propose an automatic cross-lingual alignment planning framework that integrates reasoning knowledge from different languages. |
| Outcome: | The proposed framework surpasses existing methods that require manual effort to integrate languages. |
Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown striking ability to adapt to target tasks with a few input-output demonstrations. |
| Approach: | They propose a framework which bootstraps LMs’ intrinsic capabilities to perform zero-shot ICL. |
| Outcome: | The proposed framework outperforms baselines on 23 BIG-Bench Hard tasks on average accuracy and head-to-head comparison. |
Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models (2024.emnlp-main)
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Nisarg Patel, Mohith Kulkarni, Mihir Parmar, Aashna Budhiraja, Mutsumi Nakamura, Neeraj Varshney, Chitta Baral
| Challenge: | Existing logical reasoning evaluation benchmarks focus on simplistic single-step or multi-step reasoning with limited set of inference rules. |
| Approach: | They propose to use a multi-step logical reasoning evaluation dataset to measure their ability for human-like multi- step logical thinking. |
| Outcome: | The proposed dataset covers three logic types including propositional, first-order, and non-monotonic logic with various inference rules and depths. |