Copertina

Agents Are All You Need for LLM Unlearning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sanyal, Debdeep, Mandal, Murari
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Artificial Intelligence Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Confidence is Not Competence
di: Sanyal, Debdeep, et al.
Pubblicazione: (2025)

Nine Ways to Break Copyright Law and Why Our LLM Won't: A Fair Use Aligned Generation Framework
di: Sharma, Aakash Sen, et al.
Pubblicazione: (2025)

AntiDote: Bi-level Adversarial Training for Tamper-Resistant LLMs
di: Sanyal, Debdeep, et al.
Pubblicazione: (2025)

Attention Smoothing Is All You Need For Unlearning
di: Zade, Saleh Zare, et al.
Pubblicazione: (2026)

UnStar: Unlearning with Self-Taught Anti-Sample Reasoning for LLMs
di: Sinha, Yash, et al.
Pubblicazione: (2024)

More Agents Is All You Need
di: Li, Junyou, et al.
Pubblicazione: (2024)

Contrast Is All You Need
di: Kilic, Burak, et al.
Pubblicazione: (2023)

LLM-as-a-Judge for Time Series Explanations
di: Sivalingam, Preetham, et al.
Pubblicazione: (2026)

Training on the Benchmark Is Not All You Need
di: Ni, Shiwen, et al.
Pubblicazione: (2024)

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
di: Lu, Xiaoding, et al.
Pubblicazione: (2024)

Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication
di: Ma, Weicheng, et al.
Pubblicazione: (2025)

English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training
di: Dhaliwal, Mehak, et al.
Pubblicazione: (2026)

Rho-1: Not All Tokens Are What You Need
di: Lin, Zhenghao, et al.
Pubblicazione: (2024)

Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style
di: Sanyal, Debdeep, et al.
Pubblicazione: (2025)

time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models
di: Sanyal, Debdeep, et al.
Pubblicazione: (2025)

Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs
di: Wu, Zongqian, et al.
Pubblicazione: (2025)

Tensor Product Attention Is All You Need
di: Zhang, Yifan, et al.
Pubblicazione: (2025)

Not All Documents Are What You Need for Extracting Instruction Tuning Data
di: Zhang, Chi, et al.
Pubblicazione: (2025)

Sparsity May Be All You Need: Sparse Random Parameter Adaptation
di: Rios, Jesus, et al.
Pubblicazione: (2025)

Self-Verification is All You Need To Pass The Japanese Bar Examination
di: Shin, Andrew
Pubblicazione: (2026)

Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need
di: Bai, Yuqi, et al.
Pubblicazione: (2025)

Policy Optimization Prefers The Path of Least Resistance
di: Sanyal, Debdeep, et al.
Pubblicazione: (2025)

Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning
di: Lu, Jinghui, et al.
Pubblicazione: (2025)

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
di: Bai, Yuelin, et al.
Pubblicazione: (2024)

Rethinking Data Selection at Scale: Random Selection is Almost All You Need
di: Xia, Tingyu, et al.
Pubblicazione: (2024)

Evaluation is All You Need: Strategic Overclaiming of LLM Reasoning Capabilities Through Evaluation Design
di: Sun, Lin, et al.
Pubblicazione: (2025)

Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?
di: Santosh, T. Y. S. S, et al.
Pubblicazione: (2024)

Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning
di: Som, Anirudh, et al.
Pubblicazione: (2023)

Synthetic Data RL: Task Definition Is All You Need
di: Guo, Yiduo, et al.
Pubblicazione: (2025)

Attention Is All You Need for KV Cache in Diffusion LLMs
di: Nguyen-Tri, Quan, et al.
Pubblicazione: (2025)

Measuring Representation Robustness in Large Language Models for Geometry
di: Jawandhia, Vedant, et al.
Pubblicazione: (2026)

Not All Preferences are What You Need for Post-Training: Selective Alignment Strategy for Preference Optimization
di: Dong, Zhijin
Pubblicazione: (2025)

Pipeline Parallelism is All You Need for Optimized Early-Exit Based Self-Speculative Decoding
di: Li, Ruanjun, et al.
Pubblicazione: (2025)

State and Memory is All You Need for Robust and Reliable AI Agents
di: Muhoberac, Matthew, et al.
Pubblicazione: (2025)

Pretraining on the Test Set Is No Longer All You Need: A Debate-Driven Approach to QA Benchmarks
di: Cao, Linbo, et al.
Pubblicazione: (2025)

Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
di: Goldman, Omer, et al.
Pubblicazione: (2024)

Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
di: Chen, Lingjiao, et al.
Pubblicazione: (2024)

SecEncoder: Logs are All You Need in Security
di: Bulut, Muhammed Fatih, et al.
Pubblicazione: (2024)

SurfaceLogicKV: Surface and Logic Attention Behaviors are All You Need for Robust KV Cache Compression
di: Li, Mengjie, et al.
Pubblicazione: (2025)

Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
di: Bsharat, Sondos Mahmoud, et al.
Pubblicazione: (2023)