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Leonard Lausen

7 accepted papers

2026

Knowledge Distillation for Large Language Models through Residual Learning

ICLR 2026poster

Knowledge distillation has become a crucial technique to transfer the capacities of large language models (LLMs) to smaller, more efficient models for practical deployment. While recent work exploits rich information from intermediate states of the teacher model for more effective knowledge transfer…

Cited by 0SourceScholar
2025

Understanding Silent Data Corruption in LLM Training

ACL 2025long

As the scale of training large language models (LLMs) increases, one emergent failure is silent data corruption (SDC), where hardware produces incorrect computations without explicit failure signals. In this work, we are the first to investigate the impact of real-world SDCs on LLM training by compa…

2023

Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion

AAAI 2023technical

Pretrained code language models have enabled great progress towards program synthesis. However, common approaches only consider in-file local context and thus miss information and constraints imposed by other parts of the codebase and its external dependencies. Existing code completion benchmarks al…

2023

HyTrel: Hypergraph-enhanced Tabular Data Representation Learning

NeurIPS 2023spotlight

Language models pretrained on large collections of tabular data have demonstrated their effectiveness in several downstream tasks. However, many of these models do not take into account the row/column permutation invariances, hierarchical structure, etc. that exist in tabular data. To alleviate thes…

2023

Large Language Models of Code Fail at Completing Code with Potential Bugs

NeurIPS 2023poster

Large language models of code (Code-LLMs) have recently brought tremendous advances to code completion, a fundamental feature of programming assistance and code intelligence. However, most existing works ignore the possible presence of bugs in the code context for generation, which are inevitable in…

2022

Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning

NAACL 2022long

Recent work has found that multi-task training with a large number of diverse tasks can uniformly improve downstream performance on unseen target tasks. In contrast, literature on task transferability has established that the choice of intermediate tasks can heavily affect downstream task performanc…

Cited by 20SourcePDFScholar
2017

Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model

NeurIPS 2017spotlight

With the goal of making high-resolution forecasts of regional rainfall, precipitation nowcasting has become an important and fundamental technology underlying various public services ranging from rainstorm warnings to flight safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to…

Cited by 1128SourcePDFScholar