Papers by Nicholas Lee
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement (2024.findings-acl)
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Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael Mahoney, Kurt Keutzer, Amir Gholami
| Challenge: | Pretrained large language models are currently state-of-the-art for solving most tasks . however, many of them are in the low-data regime, making fine-tuning challenging . a new data augmentation strategy uses a teacher LLM to augment a small seed dataset . |
| Approach: | They propose a targeted and iterative data augmentation strategy that augments a teacher LLM to fine-tune a small seed dataset by adding additional data. |
| Outcome: | The proposed approach outperforms fine-tuning and other data augmentation strategies on a small seed dataset. |
Evolutionary Strategies at Scale lead to Catastrophic Forgetting (2026.acl-short)
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| Challenge: | ES has been shown to improve performance on specific tasks, but it is accompanied by significant forgetting of prior abilities. |
| Approach: | They propose to use Evolutionary Strategies to train gradient-free algorithms to improve performance. |
| Outcome: | The proposed algorithm achieves performance numbers closer to GRPO for math and reasoning tasks, but forgets prior abilities. |
RExBench: Can coding agents autonomously implement AI research extensions? (2026.acl-long)
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| Challenge: | Existing large language model (LLM) agents are not capable of performing research extension tasks autonomously. |
| Approach: | They propose a benchmark to evaluate LLM agents' ability to extend existing AI research . they use extensions of 12 recently published research papers accompanied by domain expert-written instructions . |
| Outcome: | The proposed benchmark evaluates 12 LLM agents implemented using aider and OpenHands. |
TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation (2022.aacl-demo)
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Lorenz Stangier, Ji-Ung Lee, Yuxi Wang, Marvin Müller, Nicholas Frick, Joachim Metternich, Iryna Gurevych
| Challenge: | TexPrax is a messaging system to collect and annotate task-oriented dialog data . informal communication channels such as instant messengers are increasingly being used at work . |
| Approach: | They propose a messaging system that collects and annotates task-oriented dialog data from employees via chatbots. |
| Outcome: | The proposed system collects and annotates tasks-oriented dialog data from german factory workers and provides lightweight annotations. |
TinyAgent: Function Calling at the Edge (2024.emnlp-demo)
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Lutfi Erdogan, Nicholas Lee, Siddharth Jha, Sehoon Kim, Ryan Tabrizi, Suhong Moon, Coleman Hooper, Gopala Anumanchipalli, Kurt Keutzer, Amir Gholami
| Challenge: | Recent large language models (LLMs) have enabled the development of advanced agentic systems that can integrate various tools and APIs to fulfill user queries. |
| Approach: | They propose an end-to-end framework for training and deploying task-specific small language model agents capable of function calling for driving agentic systems at the edge. |
| Outcome: | The proposed model outperforms existing models by reducing the input prompt length and quantizing the inference speed. |
Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum (P18-2)
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| Challenge: | LSTMs were introduced to combat vanishing gradients in simple RNNs by augmenting them with gated additive recurrent connections. |
| Approach: | They propose to decouple the LSTM’s gates from the embedded RNN and create a new class of RNNs where the recurrence computes an element-wise weighted sum of context-independent functions of the input. |
| Outcome: | The proposed model performs as well as an LSTM on a range of problems, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients. |
Deduplicating Training Data Makes Language Models Better (2022.acl-long)
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Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini
| Challenge: | Existing language modeling datasets contain near-duplicate examples and long repetitive substrings. |
| Approach: | They develop tools that allow us to deduplicate existing language modeling datasets . they found that over 1% of the unprompted output of language models is copied verbatim . |
| Outcome: | The proposed tools reduce train-test overlap, which affects over 4% of validation sets, and improve model accuracy. |