Papers by Chi-Heng Lin
VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs (2026.acl-long)
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| Challenge: | Large language models (LLMs) are used to generate synthetic datasets but lack diversity . prior work has noted that such generated data lacks diversity - a problem that requires domain expertise. |
| Approach: | They propose a principled approach that optimizes a mathematical quantity that optimize the diversity of the dataset using determinantal point processes. |
| Outcome: | The proposed method improves diversity by 1.5-3 times compared to baseline approaches. |
FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing (2025.naacl-long)
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James Seale Smith, Chi-Heng Lin, Shikhar Tuli, Haris Jeelani, Shangqian Gao, Yilin Shen, Hongxia Jin, Yen-Chang Hsu
| Challenge: | Empirical evaluations demonstrate substantial performance gains over existing methods . |
| Approach: | They propose a method to prune LLMs that selectively prunes model blocks based on an importance score and replaces them with a low-parameter replacement strategy. |
| Outcome: | The proposed method achieves state-of-the-art performance on 5/6 and 6/6 benchmarks with a compression rate of 30% and 40%. |
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling (2024.naacl-long)
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| Challenge: | Rapid explosion in model sizes has resulted in high inference times . open-source LLMs are democratizing research in natural language processing . |
| Approach: | They propose a suite of multi-token prediction language models that reduce net inference times by leveraging traditional autoregressive weights. |
| Outcome: | The proposed model achieves same-quality generated text as baseline (Pythia-6.9B) with only 5.87% and 2.67% parameter and training time overheads. |
SLiM: Speculative Decoding with Hypothesis Reduction (2024.findings-naacl)
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| Challenge: | Speculative decoding has emerged as an alternative to autoregressive decoding for expediting inference in large language models (LLMs). prevailing assumptions focus solely on latency reduction, neglecting the computational expenses. |
| Approach: | They propose a speculative decoding enhancement to reduce the speculation set while validating more effective tokens. |
| Outcome: | The proposed method reduces the speculation set while validating more effective tokens. |