Papers by Mina Lee
Unraveling Misinformation Propagation in LLM Reasoning (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning, but how they propagate within their reasoning process remains underexplored. |
| Approach: | They propose a practical approach to mitigating misinformation propagation in LLMs by applying factual corrections early in the reasoning process and fine-tuning on synthesized data with early-stage corrections significantly improves reasoning factuality. |
| Outcome: | The proposed model can correct misinformation when explicitly instructed, but fails to correct misinformation less than half the time even with explicit instructions. |
TempLM: Distilling Language Models into Template-Based Generators (2023.findings-acl)
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| Challenge: | Pretrained language models (PLMs) have greatly improved text generation, but they have also been known to produce unfaithful or inappropriate content. |
| Approach: | They propose a pretrained language model that is a template-based generator and uses it to generate a text. |
| Outcome: | The proposed model is more faithful than the original PLM and more fluent than prior template systems. |
Enabling Language Models to Fill in the Blanks (2020.acl-main)
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| Challenge: | Infilling is the task of predicting missing spans of text at any position in a document. |
| Approach: | They propose a framework which can be used to infill entire sentences . they train off-the-shelf LMs on sequences containing concatenation of masked text . |
| Outcome: | The proposed approach can infill entire sentences on short stories, scientific abstracts, and lyrics. |
Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality (2021.naacl-main)
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| Challenge: | Existing benchmarks for lexical substitution depend on human recall as the only source of data, authors say . existing benchmarks lack coverage of the appropriate substitutes that would be most helpful to humans . |
| Approach: | They propose a benchmark for lexical substitution to find appropriate substitutes for a target word in context . existing benchmarks depend on human recall as the only source of data, they argue . |
| Outcome: | The new benchmark for lexical substitution uses a context-free thesaurus . it has 3x as many substitutes per target word for the same quality, and substitutes are 1.4x more appropriate . |