Papers by Goro Kobayashi
Transformer Language Models Handle Word Frequency in Prediction Head (2023.findings-acl)
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| Challenge: | Prediction head is a crucial component of Transformer language models. Despite its direct impact on prediction, its characteristics have been overlooked in previous analyses. |
| Approach: | They examine the inner workings of the prediction head, specifically the bias parameters, and quantify the effect of controlling their frequency biases on text generation. |
| Outcome: | The prediction head is a crucial component of the Transformer language models. |
Can Input Attributions Explain Inductive Reasoning in In-Context Learning? (2025.findings-acl)
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| Challenge: | interpreting the internal process of neural models has long been a challenge . despite rapid progress, there are still questions bridging the IA and MI eras . |
| Approach: | They propose to use input attribution methods to interpret in-context learning . they find that a certain simple IA method works best in large models . |
| Outcome: | The proposed method is the best for interpreting LLM-based ICL, but the larger the model, the harder it is to interpret it. |
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models (2021.emnlp-main)
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| Challenge: | Transformer architecture is composed of multi-head attention, which has been extensively analyzed. |
| Approach: | They extended the scope of the analysis of Transformers from solely the attention patterns to the whole attention block, i.e., multi-head attention, residual connection, and layer normalization. |
| Outcome: | The proposed method incorporates the whole attention block, i.e., multi-head attention, residual connection, and layer normalization into the analysis. |
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms (2020.emnlp-main)
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| Challenge: | Attention is a key component of Transformers, which have achieved considerable success in natural language processing. |
| Approach: | They propose to integrate attention weights and the norm of transformed input vectors into a norm-based analysis that incorporates the norm. |
| Outcome: | The proposed analysis shows that attention weights alone determine the output of attention and that reasonable word alignment can be extracted from attention mechanisms of Transformers. |
Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) take advantage of step-by-step reasoning instructions . negation is a core linguistic phenomenon that is difficult to process . |
| Approach: | They examine the step-by-step reasoning ability of large language models with a focus on negation . negation is a core linguistic phenomenon that is difficult to process . |
| Outcome: | The proposed models perform better when using chain-of-thought prompting . the results highlight unique limitations in each LLM family . |
Contrastive Learning-based Sentence Encoders Implicitly Weight Informative Words (2023.findings-emnlp)
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| Challenge: | Embedding a sentence into a point in a highdimensional continuous space plays a foundational role in the natural language processing. |
| Approach: | They propose to use contrastive loss to fine-tune sentences by inverse word frequency . they also show that more informative words receive greater weight than less informative ones . |
| Outcome: | The proposed method improves the performance of sentence embeddings by weighing them based on information-theoretic quantities. |