Papers by Yoichi Ishibashi

6 papers
LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents (2025.emnlp-main)

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Challenge: Existing approaches to optimize large language models rely on manual design or focus on optimizing individual components.
Approach: They propose a LaMDAgent framework that constructs and optimizes end-to-end post-training pipelines by exploring various model improving methods, objects, and their applied orderings based on task-based feedback.
Outcome: The proposed framework achieves a 9.0-point gain in tool-use accuracy without degrading instruction-following, and reduces computational costs.
Can Large Language Models Invent Algorithms to Improve Themselves? (2025.naacl-long)

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Challenge: Large Language Models (LLMs) have shown remarkable performance improvements, but the methods for improving LLMs are still designed by humans.
Approach: They propose a framework which enables LLMs to generate and learn model-improvement algorithms by the seed model.
Outcome: The proposed framework outperforms human-designed methods in model-improving tasks and improves the seed model by 6% and outperformed human-design methods by 4.3% on GSM8k.
Evaluating the Robustness of Discrete Prompts (2023.eacl-main)

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Challenge: Existing methods that generate discrete prompts from a small set of training instances have reported superior performance, but manual writing prompts that generalize well is challenging due to several reasons.
Approach: They propose to use discrete prompts to learn lexical constructs that would not be encountered in manually-written prompts.
Outcome: The proposed method is robust against perturbations to NLI inputs but sensitive to other types of perturbations such as shuffling and deletion of prompt tokens.
Reflection-based Word Attribute Transfer (2020.acl-srw)

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Challenge: Existing word embeddings represent analogic relations to change attributes, such as gender, such that king is male.
Approach: They propose a method for word attribute transfer based on reflection mappings without such an analogy operation.
Outcome: The proposed method can transfer attributes of the given words without changing the words that do not have the target attributes.
Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer Review (2026.findings-acl)

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Challenge: a growing workload has made peer review automation an urgent necessity, says a new study . official conference guidelines and reviewer-imitating guidelines degraded review performance . current human-based peer review system faces serious challenges, authors say .
Approach: They analyze how reviewer guidelines influence automated peer review . official conference guidelines produce review results consistent with human judgments .
Outcome: The proposed reviewer guidelines produce results consistent with human judgments . the proposed reviewers' imitations degraded performance, the authors note .
Subspace Representations for Soft Set Operations and Sentence Similarities (2024.naacl-long)

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Challenge: Embedding-based word representations are crucial for capturing the semantic meanings of individual words.
Approach: They propose to embed word sets and corresponding set operations within pre-trained word embedding spaces.
Outcome: The proposed representations outperform vector-based representations in sentence similarity and set retrieval tasks.

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