Annisa Maulida Ningtyas Universitas Gadjah Mada, Guntur Budi Herwanto, Yunita Sari Universitas Gadjah Mada, Rifki Afina Putri Universitas Gadjah Mada, Filip Kovacevic Technische Universität Wien, Alaa El-Ebshihy Technische Universität Wien, Varvara Arzt, Florina Piroi Technische Universität Wien
This paper introduces AnnoHID, a semi-automated annotation framework designed for medical texts in low-resource languages. The system integrates large language models (LLMs) for pre-annotation and human validation to support efficient and consistent annotation. We demonstrate its application to Bahasa Indonesia medical social media texts from Alodokter, a medical Q A platform, for Named Entity Recognition (NER) and Medical Concept Normalization (MCN). We conducted a user study with 21 participants to demonstrate the effectiveness of AnnoHID. The results show that LLM-assisted annotation yields higher inter-annotator agreement for both NER (\kappa = 0.76) and MCN (\kappa = 0.63) and that human review improves raw LLM NER output, raising the F1 score from 0.39 to 0.45. However, LLM assistance did not reduce annotation time and may introduce normalization bias in MCN. The framework is multilingual, human-in-the-loop, and interoperable with standard medical terminologies, such as SNOMED-CT. Future work focuses on mitigating pre-annotation bias, reducing annotation overhead, and scaling evaluations to larger datasets and additional low-resource languages.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表
Zhang Zhang Peking University, Shuqi Lu BAAI, Hongjin Qian, Di He Peking University and Microsoft, Zheng Liu
Building LLM-based agents has become increasingly important. Recent works on LLM-based agent self-evolution primarily record successful experiences as textual prompts or reflections, which cannot reliably guarantee efficient task re-execution in complex scenarios. We propose AgentFactory, a new self-evolution paradigm that preserves successful task solutions as executable subagent code rather than textual experience. Crucially, these subagents are continuously refined based on execution feedback, becoming increasingly robust and efficient as more tasks are encountered. Saved subagents are pure Python code with standardized documentation, enabling portability across any Python-capable system. We demonstrate that AgentFactory enables continuous capability accumulation: its library of executable subagents grows and improves over time, progressively reducing the effort required for similar tasks without manual intervention. Our implementation is open-sourced at https://github.com/zzatpku/AgentFactory, and our demonstration video is available at https://youtu.be/iKSsuAXJHW0.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表
Jiameng Huang, Zhi Zhang, Zhenyu He, Jiacheng Sun Huawei Noah's Ark Lab, Di He Peking University and Microsoft
Lifelong learning investigates how models adapt when exposed to a potentially infinite stream of data. Most conventional approaches focus on updating model parameters (i.e., the neural network weights) as the underlying data distribution evolves over time. However, in natural language processing, model parameters are not the only components that matter. The tokenizer, a foundational part of the system, is usually assumed to remain fixed in lifelong learning scenarios. In this work, we challenge the validity of this assumption: as language evolves, a static tokenizer fragments newly emerging lexical items, reducing compression efficiency and consequently degrading the model performance. We introduce the Temporal Drift Tokenizer (Ted-Tok), which maintains an evolving vocabulary that adapts to emerging linguistic patterns over time. This adaptivity is driven by time-weighted frequency estimators that smooth short-term fluctuations to capture persistent linguistic trends, and a principled addition-deletion strategy targeting sink tokens. Across multiple domains, Ted-Tok consistently improves compression and task performance, with gains increasing under stronger drift, underscoring the role of tokenizer adaptivity in lifelong learning.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Guy Yariv Wix and Hebrew University of Jerusalem, Idan Schwartz Bar-Ilan University, Yossi Adi Hebrew University of Jerusalem and Meta, Sagie Benaim Hebrew University of Jerusalem
Commonsense reasoning often requires both textual and visual knowledge, yet Large Language Models (LLMs) trained solely on text lack visual grounding (e.g., “what color is an emperor penguin’s belly?”). Visual Language Models (VLMs) perform better on visually grounded tasks but face two limitations: (i) often reduced performance on text-only commonsense reasoning compared to text-trained LLMs, and (ii) adapting newly released LLMs to vision input typically requires costly multimodal training. An alternative augments LLMs with test-time visual signals, improving visual commonsense without harming textual reasoning, but prior designs often rely on early fusion and a single image, which can be suboptimal. We propose a late multi-image fusion method: multiple images are generated from the text prompt with a lightweight parallel sampling, and their prediction probabilities are combined with those of a text-only LLM through a late-fusion layer that integrates projected visual features just before the final prediction. Across visual commonsense and NLP benchmarks, our method significantly outperforms augmented LLMs on visual reasoning, matches VLMs on vision-based tasks, and, when applied to strong LLMs such as LLaMA 3, also improves NLP performance while adding only modest test-time overhead.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表