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Mingfeng Xue

7 accepted papers

2025

START: Self-taught Reasoner with Tools

EMNLP 2025

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations. Integrating computational tools with LRMs remains challenging, particularly in activating and enhancing

2025

Teaching Language Models to Reason with Tools

NeurIPS 2025poster

Large reasoning models (LRMs) like OpenAI-o1 have shown impressive capabilities in natural language reasoning. However, these models frequently demonstrate inefficiencies or inaccuracies when tackling complex mathematical operations. While integrating computational tools such as Code Interpreters (C…

Cited by 0SourcecodeScholar
2024

How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition

ACL 2024long

Large language models (LLMs) with enormous pre-training tokens and parameters emerge diverse abilities, including math reasoning, codegeneration, and instruction following. These abilities are further enhanced by supervised fine-tuning (SFT). While the open-source community has explored ad-hoc SFT f…

2024

Rationales for Answers to Simple Math Word Problems Confuse Large Language Models

ACL 2024findings

Recently, large language models (LLMs) have demonstrated breakthrough mathematical problem-solving capabilities in grade school math word problems (MWP). For example, on the MWP benchmark GSM8K, the accuracy of GPT-3.5-Turbo and MetaMath-70B reaches 80.80% and 82.30%, respectively. One question aris…

Cited by 0SourcePDFScholar
2023

Dynamic Voting for Efficient Reasoning in Large Language Models

EMNLP 2023long findings

Multi-path voting methods like Self-consistency have been used to mitigate reasoning errors in large language models caused by factual errors and illusion generation. However, these methods require excessive computing resources as they generate numerous reasoning paths for each problem. And our expe…

Cited by 0SourceScholar
2023

Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation

ACL 2023long

Attribute-based Controlled Text Generation (CTG) refers to generating sentences that satisfy desirable attributes (e.g., emotions and topics). Existing work usually utilize fine-tuning or resort to extra attribute classifiers, yet suffer from increases in storage and inference time. To address these…

2023

Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation

EMNLP 2023long main

Unsupervised paraphrase generation is a challenging task that benefits a variety of downstream NLP applications. Current unsupervised methods for paraphrase generation typically employ round-trip translation or denoising, which require translation corpus and result in paraphrases overly similar to t…

Cited by 0SourceScholar