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新论文 · v1arXiv:2611.10977

DM-Call: Decision-Model-Guided Function Calling with Cumulative Verification Feedback

Minhyeong Yu, Wonduk Seo, Seunghyun Lee

原文摘要

Reliable function calling requires Large Language Models (LLMs) to select appropriate functions and generate arguments consistent with user intent. However, syntactically valid calls may still be semantically incorrect due to inappropriate function selection or mismatched arguments. In this paper, we propose Decision-Model-Guided Function Calling (DM-Call), a framework that combines local schema validation with separate decision-model verification of function selection and argument correctness. For rejected candidates, DM-Call accumulates verification probabilities, binary judgments, and schema errors across attempts. A hybrid feedback representation further combines numerical probabilities with verbal correctness labels to guide iterative refinement without executing external tools. Extensive evaluation across BFCL and API-Bank (first-call) demonstrates DM-Call boosts function-calling accuracy versus direct generation, at modest additional cost.

cs.IR · cs.AI · cs.CL · cs.LG · cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.18639

Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs

Seunghyun Lee, Dongyoon Han, Sangdoo Yun

原文摘要

In a dual-use field, hazardous knowledge and the benign expertise around it share vocabulary, concepts, and causal structure. That overlap makes selective refusal hard, and existing methods such as unlearning and refusal training overgeneralize their suppression into the benign side. We argue for conditioning, not suppression. In this work, we introduce Token Inoculation, which marks hazardous sentences with a special token, the inoculation token, during continued pre-training. We then fine-tune the model to refuse hazardous questions and, on the same questions under the inoculation token, to predict the hazardous answers. At test time the inoculation token is absent, so the model does not express the hazardous answers it learned to predict. Across 1.5B to 14B parameters and three architecture families it holds hazardous accuracy near 18% while retaining 93% of the instruct baseline's MMLU performance, the best safety-utility balance among the baselines we compare. The same advantage reproduces under open-ended generation and in a cybersecurity transfer. We test this with a causal do(X) analysis that randomizes the accuracy of the conditioning signal, and find that refusal becomes more selective the more of the hazardous domain the model still knows. Our findings underscore the importance of training to predict the responses that must be refused, and outline a direction for safety alignment that does not degrade models' growing frontier knowledge. Project page: https://token-inoculation.github.io/

cs.LG · cs.CL

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1039ACL Anthology · ACL 2026

SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science

Wonduk Seo Enhans and Peking University, Juhyeon Lee, Yanjun Shao, Qingshan Zhou Peking University, Seunghyun Lee Enhans, Yi Bu Peking University

原文摘要

Large Language Models (LLMs) have enabled dynamic reasoning in automated data analytics, yet recent multi-agent systems remain limited by rigid, single-path workflows that restrict strategic exploration and often lead to suboptimal outcomes. To overcome these limitations, we propose SPIO (Sequential Plan Integration and Optimization), a framework that replaces rigid workflows with adaptive, multi-path planning across four core modules: data preprocessing, feature engineering, model selection, and hyperparameter tuning. In each module, specialized agents generate diverse candidate strategies, which are cascaded and refined by an optimization agent. SPIO offers two operating modes: SPIO-S for selecting a single optimal pipeline, and SPIO-E for ensembling top-k pipelines to maximize robustness. Extensive evaluations on Kaggle and OpenML benchmarks show that SPIO consistently outperforms state-of-the-art baselines, achieving an average performance gain of 5.6%. By explicitly exploring and integrating multiple solution paths, SPIO delivers a more flexible, accurate, and reliable foundation for automated data science.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表