Papers by Andy Liu
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities (2022.acl-long)
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Hsiang-Sheng Tsai, Heng-Jui Chang, Wen-Chin Huang, Zili Huang, Kushal Lakhotia, Shu-wen Yang, Shuyan Dong, Andy Liu, Cheng-I Lai, Jiatong Shi, Xuankai Chang, Phil Hall, Hsuan-Jui Chen, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-yi Lee
| Challenge: | Existing evaluation methods for transfer learning are limited in speech research . authors show that pre-trained models transfer well across multiple tasks . |
| Approach: | They propose a benchmark to evaluate pre-trained models by increasing task diversity and difficulty over SUPERB. |
| Outcome: | The proposed benchmark increases task diversity and difficulty over SUPERB-SG. |
Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism (D18-1)
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| Challenge: | Pronouns are often omitted in pro-drop languages, such as Chinese . this leads to various translation problems in terms of completeness, syntax and semantics . |
| Approach: | They propose a reconstruction-based approach to alleviate dropped pronoun (DP) translation problems for neural machine translation models by employing a shared reconstructor and a joint learning approach. |
| Outcome: | The proposed approach improves translation performance and accuracy of DP predictions. |
Evaluating Large Language Model Biases in Persona-Steered Generation (2024.findings-acl)
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| Challenge: | a recent wave of powerful new large language models has raised concerns that their expressed opinions may be biased towards certain political, national or moral viewpoints. |
| Approach: | They define an incongruous persona as a persona with multiple traits where one trait makes its other traits less likely in human survey data. |
| Outcome: | The results show that LLMs are less steerable towards incongruous personas than congruous ones . the models that are fine-tuned with RLHF are more steerable, especially towards stances associated with political liberals and women . |
PLACES: Prompting Language Models for Social Conversation Synthesis (2023.findings-eacl)
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Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu, Dilek Hakkani-Tur
| Challenge: | Currently, collecting high quality conversational data is expensive and infeasible for many applications . a promising direction is to generate synthetic dialogues by prompting large language models . |
| Approach: | They propose to use expert-written conversations as in-context examples to generate synthetic dialogues by prompting large language models. |
| Outcome: | The proposed approach is generalizable to multi-party conversations, compared to human-collected conversations. |
BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data (2025.acl-long)
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| Challenge: | Existing methods for embedding human personality traits into LLMs are limited by realism and validity issues. |
| Approach: | They propose to use a large-scale dataset to embed human personality traits into LLMs . they use supervised fine-tuning and direct preference optimization to train LLM models . |
| Outcome: | The proposed methods outperform prompting on personality assessments and IPIP-NEO, and show higher conscientiousness, agreeableness, lower extraversion, and lower neuroticism on reasoning tasks. |
Tailoring Neural Architectures for Translating from Morphologically Rich Languages (C18-1)
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| Challenge: | A morphologically complex word is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures. |
| Approach: | They propose a neural architecture which is designed to deal with morphological complexities on the source side and redesign the decoder accordingly to benefit from such information. |
| Outcome: | The proposed model outperforms existing subword- and character-based architectures and showed significant improvements on translating from German, Russian, and Turkish into English. |
Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation (N18-1)
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| Challenge: | Morphologically complex words (MCWs) are multi-layer structures consisting of different subunits, each of which carries semantic information and has a specific syntactic role. |
| Approach: | They propose an extension to the state-of-the-art model which works at the character level and boosts the decoder with target-side morphological information. |
| Outcome: | The proposed model improves on the state-of-the-art model and can be extended to include morphologically complex words (MCWs) in three languages. |