| Challenge: | In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. |
| Approach: | They propose a new chain of thought prompting method that enhances LLMs’ reasoning ability through chain of thinking prompting, including the original chain-of-thought prompting and least-to-most prompting. |
| Outcome: | The proposed method brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gain, versus the least-to-most prompting. |
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Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters (2023.acl-long)
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| Challenge: | Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). |
| Approach: | They propose to use Chain-of-Thought (CoT) prompting to encourage the LLM to generate intermediate rationales for solving a problem by providing a series of reasoning steps in the demonstrations. |
| Outcome: | The proposed model can generate coherent lines of reasoning even with invalid demonstrations while still generating coherent lines during inference. |
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models (2024.findings-naacl)
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| Challenge: | Chain-of-thought (CoT) prompting is a technique to enhance the reasoning abilities of Large language models (LLMs) however, the reasoning chains of demonstrations are observed to be prone to errors, which can lead to incorrect reasoning during inference. |
| Approach: | They propose an iterative bootstrapping technique to enhance the reasoning abilities of Large language models (LLMs) by generating a series of reasoning steps to obtain the answer, and using the reasoning chains as exemplars to demonstrate the task. |
| Outcome: | The proposed method improves the performance of Large language models (LLMs) on three reasoning tasks on ten datasets. |
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs (2023.findings-emnlp)
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| Challenge: | Existing LLMs generate responses based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context. |
| Approach: | They propose a linguistic cue-based chain-of-thoughts method which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue. |
| Outcome: | The proposed method outperforms standard prompting methods on in-depth dialogue questions and linguistic cues exhibited in the context. |
ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting (2024.lrec-main)
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| Challenge: | Existing CoT synthesis approaches focus on simpler reasoning tasks and result in inconsistent CoT prompts. |
| Approach: | They propose a framework for automatic generation of superior CoT prompts based on three major evolution strategies . they propose 'step-level debating' method where multiple debaters discuss each reasoning step to arrive at the correct answer. |
| Outcome: | The proposed framework can generate superior CoT prompts from a CoT dataset. |
What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study (2023.findings-emnlp)
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| Challenge: | Using a few-shot prompt, we examine the effects of symbols and patterns on in-context learning in large language models. |
| Approach: | They employ a counterfactual prompting approach by manipulating examples and testing the consequences on model behavior. |
| Outcome: | The proposed approach allows us to understand the relative contributions of symbols and patterns on in-context learning. |
CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks (2023.emnlp-main)
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| Challenge: | Chain-of-Thought prompting is popular in reasoning tasks, but its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored. |
| Approach: | They propose a Coarse-to-Fine Chain-of-Thought approach that breaks down NLU tasks into multiple reasoning steps where LLMs can learn to acquire essential concepts. |
| Outcome: | The proposed approach is effective in assisting the LLMs adapt to multi-grained NLU tasks under zero-shot and few-shot multi-domain settings. |
Active Prompting with Chain-of-Thought for Large Language Models (2024.acl-long)
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| Challenge: | Existing methods to annotate large language models rely on a fixed set of human-annotated exemplars, which are not always the most effective for different tasks. |
| Approach: | They propose a method to adapt large language models to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning) they introduce several metrics to characterize uncertainty so as to select the most uncertain questions for annotation. |
| Outcome: | The proposed method significantly improves performance on eight complex reasoning tasks. |
R3 Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context (2023.findings-emnlp)
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| Challenge: | Existing studies have evaluated LLMs under noise-free context but the dilemma for LLM to produce inaccurate results under noisy context has not been fully investigated. |
| Approach: | They propose a new method for CoT reasoning using Chain-of-Thought prompting that interacts with LLMs to perform key sentence extraction, variable declaration and answer prediction. |
| Outcome: | The proposed method outperforms existing CoT prompting methods on five reasoning tasks under noisy context. |
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models (2023.acl-long)
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| Challenge: | Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. |
| Approach: | They propose a plan-and-solve (PS) prompting that includes a few manual steps to generate reasoning steps and improves the quality of generated reasoning steps. |
| Outcome: | The proposed strategy outperforms Zero-shot-CoT on ten reasoning problems and has comparable performance to 8-shot CoT prompting on the math reasoning problem. |
Tab-CoT: Zero-shot Tabular Chain of Thought (2023.findings-acl)
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| Challenge: | Recent efforts to encourage more structured reasoning procedures to be captured have shown that chain-of-though (CoT) prompting methods can be effective in NLP tasks. |
| Approach: | They propose a tabular-format CoT prompting method that allows the complex reasoning process to be explicitly modeled in a highly structured manner. |
| Outcome: | The proposed method shows impressive performance improvements on a range of reasoning tasks. |