Papers by Tyler A. Chang
Correlations between Multilingual Language Model Geometry and Crosslingual Transfer Performance (2024.lrec-main)
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| Challenge: | Pre-trained multilingual language models represent multiple languages in a single vector space, a feature hypothesized to enable impressive crosslingual transfer capabilities. |
| Approach: | They propose to use a multilingual representation space that sorts axes based on their language-separability to determine whether geometric distances between languages correlate with crosslingual transfer performance. |
| Outcome: | The proposed measures do not generalize well across models, layers, and tasks. |
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics (2024.lrec-main)
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Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann, Nithum Thain, Erin MacMurray van Liemt, Kathleen Meier-Hellstern, Lucas Dixon
| Challenge: | a growing audience of users is engaging with LLM-driven chatbots. |
| Approach: | They propose a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia’s Neutral Point of View principle. |
| Outcome: | The proposed methods detect errors in the tuned LLM responses even when no training data is available. |
Word Acquisition in Neural Language Models (2022.tacl-1)
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| Challenge: | Language models acquire individual words during training, based on unigram token frequencies, before transitioning loosely to bigram probabilities, eventually converging on more nuanced predictions. |
| Approach: | They examine how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words on the MacArthur-Bates Communicative Development Inventory. |
| Outcome: | The models follow consistent patterns during training for both unidirectional and bidirectional models, and for both LSTM and Transformer architectures. |
On the Acquisition of Shared Grammatical Representations in Bilingual Language Models (2025.acl-long)
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| Challenge: | Crosslingual transfer is crucial to contemporary language models’ multilingual capabilities, but how it occurs is not well understood. |
| Approach: | They use structural priming to study grammatical representations in humans by controlling for training data quantity and language exposure. |
| Outcome: | The proposed model is able to learn a language in two languages and has a higher likelihood of learning a prepositional object (PO) dative sentence than a double object (DO) . |