Papers by Zhihong Shao
Learning Task Decomposition to Assist Humans in Competitive Programming (2024.acl-long)
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| Challenge: | Using language models (LMs) to solve complex problems, humans might struggle to understand and repair flawed ones. |
| Approach: | They propose to automatically decompose complex problems into simpler pieces that correspond to specific subtasks and measure their assistive value. |
| Outcome: | The proposed method enables non-experts to solve 33.3% more problems and speeds them up by 3.3x . |
A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering (2021.acl-long)
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| Challenge: | Weakly supervised question answering usually has only final answers as supervision signals while correct solutions are not provided. |
| Approach: | They propose to explicitly exploit the semantic correlations between question-answer pairs and predicted answers by maximizing mutual information between question and answer pairs. |
| Outcome: | The proposed method significantly outperforms previous learning methods in terms of task performance and is more effective in training models to produce correct solutions. |
Chaining Simultaneous Thoughts for Numerical Reasoning (2022.findings-emnlp)
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| Challenge: | Numerical reasoning over text is an essential skill for AI systems . structure modeling is effective, but structures restrict how a model should grasp the reasoning process . |
| Approach: | They propose a numerical reasoner that models reasoning steps using a directed acyclic graph without pre-defined decoding dependencies. |
| Outcome: | The proposed model produces diverse reasoning steps without pre-defined dependencies and compares relevant ones to reach a solution. |
Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework (2022.acl-long)
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| Challenge: | Existing approaches to open-domain question answering use a rerank-then-read framework . existing approaches use reranked evidence to predict multiple valid answers . |
| Approach: | They propose to use a recall-then-verify framework to solve open-domain questions . the framework separates the reasoning process of each answer to make better use of retrieved evidence . |
| Outcome: | The proposed framework predicts significantly more gold answers on open-domain questions than existing systems that use an oracle reranker. |
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model (D19-1)
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| Challenge: | Existing methods for data-to-text generation are insufficient to produce long and diverse texts. |
| Approach: | They propose a planning-based hierarchical variational model that plans a sequence of groups and then realizes each sentence conditioned on the planning result and the previously generated context. |
| Outcome: | The proposed model outperforms state-of-the-art models in long and diverse text generation. |
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy (2023.findings-emnlp)
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| Challenge: | Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation. |
| Approach: | They propose to have large language models actively involved in retrieval to guide retrieval with generation. |
| Outcome: | The proposed method synergizes retrieval and generation in an iterative manner, and can generate better results in subsequent iterations. |
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations (2024.acl-long)
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| Challenge: | Existing methods for process-oriented math reward models rely on manual annotation. |
| Approach: | They propose a process-oriented math process reward model called Math-shepherd which assigns a reward score to each step of math problem solutions. |
| Outcome: | The proposed model breaks the bottleneck of manual supervision in two scenarios. |