Making Language Models Better Reasoners with Step-Aware Verifier

Li, Yifei; Lin, Zeqi; Zhang, Shizhuo; Fu, Qiang; Chen, Bei; Lou, Jian–Guang; Chen, Weizhu

doi:10.18653/v1/2023.acl-long.291

Cited by 25 publications

(17 citation statements)

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“…This approach is akin to human reflection and involves critically evaluating each step of the reasoning process. Various verify-based methods [57,93,98,163,211,217] have been proposed to address these issues.…”

Section: Advanced Cot Methodsmentioning

confidence: 99%

“…To mitigate the challenge of validating the entire deductive reasoning process, VerifyCoT [98] introduces a deductive reasoning form, ensuring that each reasoning step strictly relies on the preceding steps. Furthermore, DIVERSE [93] independently verifies each reasoning step and a voting mechanism to eliminate incorrect answers. Both Verifyand-Edit [211] and Retrieval-CoT [57] utilizes external retrieval tools to support the model in validating reasoning rationales.…”

Section: Advanced Cot Methodsmentioning

confidence: 99%

“…Due to the inherent stochastic nature of language models, which output probability distributions over predicted words, they may randomly generate incorrect reasoning steps and outcomes. To tackle this challenge, some methods [27,43,79,93,115,124,182,198] leverage the concept of ensemble learning, employing techniques such as voting and ranking to eliminate uncertainties in the reasoning process.…”

Section: Advanced Cot Methodsmentioning

confidence: 99%

“…Complexity-based CoT [43] favors answers derived from more intricate reasoning paths. DIVERSE [93] uses a weighted voting mechanism to filter out incorrect answers. Nevertheless, these voting-based methods often overlook the potentially useful information within unsuccessful CoT reasoning and lack an efficient integration of multiple reasoning chains to improve performance.…”

Section: Advanced Cot Methodsmentioning

confidence: 99%

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Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Section: Advanced Cot Methodsmentioning

confidence: 99%

Section: Advanced Cot Methodsmentioning

confidence: 99%

Section: Advanced Cot Methodsmentioning

confidence: 99%

Section: Advanced Cot Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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“…Uesato et al (2022) found that process supervision -correctness of the rationale -enhances the performance of fine-tuned LLMs relative to outcome supervision -whether the answer is correct or not. Subsequent work correspondingly studied ways of deriving reward sig- nals for individual reasoning steps (Li et al, 2023;Lightman et al, 2024;Yu et al, 2023), combining solution-level and step-level verifiers (Zhu et al, 2023), and augmenting verifiers with auxiliary information, such as results of program execution (Ni et al, 2023b). In Ma et al (2023); Wang et al (2023b), rationale generation is treated as a graph search problem, either using a stepwise verifier to guide the search or estimating the quality of steps by Monte Carlo rollouts.…”

Section: Related Workmentioning

confidence: 99%

Understanding by Understanding Not: Modeling Negation in Language Models

Hosseini¹,

Reddy²,

Bahdanau³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language models often handle negation incorrectly. To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus. By training BERT with the resulting combined objective we reduce the mean top 1 error rate to 4% on the negated LAMA dataset. We also see some improvements on the negated NLI benchmarks.

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GenQREnsemble: Zero-Shot LLM Ensemble Prompting for Generative Query Reformulation

Dhole,

Agichtein

2024

Lecture Notes in Computer Science

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Making Language Models Better Reasoners with Step-Aware Verifier

Cited by 25 publications

References 0 publications

Geography of Technology Transfer in China

Geography of Technology Transfer in China

Understanding by Understanding Not: Modeling Negation in Language Models

GenQREnsemble: Zero-Shot LLM Ensemble Prompting for Generative Query Reformulation

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