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FLLM 2026: International Symposium on Foundation and Large Language Models

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FLLM
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投稿締切日:
2026-08-21 Extended
通知日:
2026-10-01
開催日:
2026-11-17
開催地:
Barcelona, Spain
開催回数:
4
閲覧: 10586   フォロー: 0   参加: 0

会伴インデックス (CP-I)

43.0 / 100
全 5,682 件中 第 3,758 位 · 上位 67%

自然言語処理・音声 分野 85 件中 第 58 位 人工知能・機械学習 分野 739 件中 第 477 位

学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
43
コミュニティの注目度 (10%)
16
公開情報の充実度 (15%)
35

使用した入力: 確認できる開催回数：4 · 過去 24 か月にこのページを開いた研究者：7 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 過去の開催回 (+3.0) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-19

論文募集

FLLM 2026 (International Symposium on Foundation and Large Language Models) is an academic conference held in Barcelona, Spain on 2026-11-17. The paper submission deadline is 2026-08-21 (extended). Acceptance notifications are sent on 2026-10-01.

With the emergence of foundation models (FMs) and Large Language Models (LLMs) that are trained on large amounts of data at scale and adaptable to a wide range of downstream applications, Artificial intelligence is experiencing a paradigm revolution. BERT, T5, ChatGPT, GPT-4, Falcon 180B, Codex, DALL-E, Whisper, and CLIP are now the foundation for new applications ranging from computer vision to protein sequence study and from speech recognition to coding. Earlier models had a reputation of starting from scratch with each new challenge. The capacity to experiment with, examine, and comprehend the capabilities and potentials of next-generation FMs is critical to undertaking this research and guiding its path. Nevertheless, these models are currently inaccessible as the resources required to train these models are highly concentrated in industry, and even the assets (data, code) required to replicate their training are frequently not released due to their demand in the real-time industry. At the moment, mostly large tech companies such as OpenAI, Google, Facebook, and Baidu can afford to construct FMs and LLMS. Despite the expected widely publicized use of FMs and LLMS, we still lack a comprehensive knowledge of how they operate, why they underperform, and what they are even capable of because of their emerging global qualities. To deal with these problems, we believe that much critical research on FMs and LLMS would necessitate extensive multidisciplinary collaboration, given their essentially social and technical structure. The International Conference on Foundation and Large Language Models (FLLM) addresses the architectures, applications, challenges, approaches, and future directions. We invite the submission of original papers on all topics related to FLLMs, with special interest in but not limited to: Architectures and Systems Transformers and Attention Bidirectional Encoding Autoregressive Models Massive GPU Systems Prompt Engineering Multimodal LLMs Fine-tuning Challenges Hallucination Cost of Creation and Training Energy and Sustainability Issues integration Safety and Trustworthiness Interpretability Fairness Social Impact Future Directions Generative AI Explainability and EXplainable AI Retrieval Augmented Generation (RAG) Federated Learning for FLLM Large Language Models Fine-Tuning on Graphs Data Augmentation Natural Language Processing Applications Generation Summarization Rewrite Search Question Answering Language Comprehension and Complex Reasoning Clustering and Classification Applications Natural Language Processing Communication Systems Security and Privacy Image Processing and Computer Vision Life Sciences Financial Systems
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