Papers by Jia Peng Lim
Disentangling Transformer Language Models as Superposed Topic Models (2023.emnlp-main)
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| Challenge: | Topic Modelling is an established area of research where the quality of a given topic is measured using coherence metrics. |
| Approach: | They propose a weight-based approach to search and disentangle decoder-only TLM by a Wikipedia corpus. |
| Outcome: | The proposed approach can be applied to GPT-2 models and LLaMA models. |
Towards Reinterpreting Neural Topic Models via Composite Activations (2022.emnlp-main)
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| Challenge: | Most Neural Topic Models (NTMs) use a variational auto-encoder framework producing K topics limited to the size of the encoder’s output. |
| Approach: | They propose a model-free two-stage process to reinterpret NTM and derive further insights on the state of the trained model. |
| Outcome: | The proposed model-free process decouples the strict interpretation of topics from the original NTM and evaluates them on a large external corpus. |
Large-Scale Correlation Analysis of Automated Metrics for Topic Models (2023.acl-long)
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| Challenge: | Existing studies on topic models lack a correlation between automated coherence metrics and human judgement. |
| Approach: | They propose a sampling approach to mine topics for metric evaluation and extend the analysis to measure topical differences between corpora. |
| Outcome: | The proposed method extends to measure topical differences between corpora and human judgement by using extensive user study. |
Interpreting Topic Models in Byte-Pair Encoding Space (2025.coling-main)
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| Challenge: | Byte-pair encoding (BPE) is a popular method of tokenizing valid words onto a token space V b with a predetermined fixed size, and handling out-of-vocabulary words, breaking words into smaller tokens. |
| Approach: | They propose to interpret the recovery of valid words from these tokens as a ranking problem and apply existing evaluation measures to topic sets. |
| Outcome: | The proposed model interprets the recovery of valid words from these tokens as a ranking problem and applies existing evaluation measures. |