Papers by Guy Dar
LM-Debugger: An Interactive Tool for Inspection and Intervention in Transformer-Based Language Models (2022.emnlp-demos)
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| Challenge: | Transformer-based language models (LMs) are opaque and unexplained, causing problems for endusers and developers who wish to debug or fix their behaviour. |
| Approach: | They propose an interactive debugger tool for transformer-based LMs that provides a fine-grained interpretation of the model's internal prediction process and a powerful framework for intervening in LM behavior. |
| Outcome: | The proposed tool provides a fine-grained interpretation of the model's internal prediction construction process, and a powerful framework for intervening in LM behavior. |
In-context Learning and Gradient Descent Revisited (2024.naacl-long)
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| Challenge: | In-context learning (ICL) has shown impressive results in few-shot learning tasks, yet its underlying mechanism remains elusive. |
| Approach: | They propose a simple gradient descent-based optimization procedure that respects layer causality and improves similarity scores significantly. |
| Outcome: | The proposed procedure improves similarity scores on untrained models despite not showing ICL. |
Analyzing Transformers in Embedding Space (2023.acl-long)
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| Challenge: | Understanding Transformer-based models has attracted significant attention . a zero-pass approach is feasible for some parameters, and for two-layer attention networks . |
| Approach: | They propose a theoretical framework where parameters of a trained Transformer are interpreted by projecting them into the embedding space. |
| Outcome: | The proposed framework shows that pre-trained and fine-tuned models can be interpreted in embedding space. |