| --- |
| license: other |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - text-generation |
| license_name: mixed-license |
| license_link: LICENSE.md |
| tags: |
| - llm-agents |
| - deployment-time-learning |
| - continual-learning |
| configs: |
| - config_name: alfworld |
| data_files: alfworld/alfworld.jsonl |
| - config_name: banking77 |
| data_files: banking77/banking77.jsonl |
| - config_name: bird |
| data_files: bird/bird.jsonl |
| - config_name: cmdl |
| data_files: cmdl/cmdl.jsonl |
| - config_name: ddxplus |
| data_files: ddxplus/ddxplus.jsonl |
| - config_name: lfd |
| data_files: lfd/lfd.jsonl |
| - config_name: mud |
| data_files: mud/mud.jsonl |
| - config_name: rca |
| data_files: rca/rca.jsonl |
| - config_name: scienceworld |
| data_files: scienceworld/scienceworld.jsonl |
| - config_name: sentifin |
| data_files: sentifin/sentifin.jsonl |
| - config_name: spider |
| data_files: spider/spider.jsonl |
| - config_name: 2wiki |
| data_files: 2wiki/2wiki.jsonl |
| - config_name: ehr |
| data_files: ehr/ehr.jsonl |
| --- |
| |
| # DTLBench |
|
|
| <p align="center"> |
| <a href="https://github.com/guosyjlu/CASCADE"> |
| 💻 GitHub Repo |
| </a> | |
| <a href="https://physionet.org/content/mimic-iv-ext-dtlbench/1.0.0/"> |
| 🫀 DTLBench PhysioNet (To be released) |
| </a> | |
| <a href="https://arxiv.org/abs/2605.06702"> |
| 📄 Paper |
| </a> |
| </p> |
|
|
| DTLBench is a benchmark for **deployment-time learning** of large language model agents. It collects diverse task streams spanning medical diagnosis, legal analysis, operational reasoning, financial prediction, text-to-SQL, embodied decision making, tabular reasoning on EHRs, deep search, etc. |
|
|
| The dataset was introduced in the paper: [CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment](https://arxiv.org/abs/2605.06702). |
|
|
| ## Benchmark Overview |
|
|
| - Total tasks: `16` (3 of them will be released through PhysioNet) |
| - Data format: one JSON object per line (`.jsonl`) |
| - Primary use case: benchmark streams for deployment-time learning |
|
|
| DTLBench covers three environment styles used in CASCADE: |
|
|
| - `single-turn`: one input, one final answer |
| - `multi-turn`: sequential interaction with the environment |
|
|
| ### Summary statistics of the DTLBench. The maximum steps refer to the maximum number of interaction steps that the environment allows per task. |
|
|
| | **Property** | **Domain** | **Task** | **Dataset** | **Maximum Steps** | **Number of Samples** | |
| |------------------------------|---------------------|--------------------------------------------------|-------------------|-------------------|-----------------------| |
| | **Single-turn** | **Medical** | Medical Diagnosis | DDXPlus | 1 | 3136 | |
| | | | Medication Recommendation | MIMIC-IV-MR | 1 | 2881 | |
| | | | Medical Specialty Referral | MIMIC-IV-MSR | 1 | 2115 | |
| | | | Triage Level Prediction | MIMIC-IV-TLP | 1 | 2200 | |
| | | **Legal** | Multi-Defendant Legal Charge Prediction | MUD | 1 | 1740 | |
| | | | Penalty Legal Prediction | CMDL | 1 | 2080 | |
| | | **Financial** | Financial Customer Intent Routing | Banking77 | 1 | 5000 | |
| | | | Entity-Aware Financial Sentiment Analysis | SEntFiN | 1 | 2299 | |
| | | **AIOps** | AIOps Root Cause Analysis | RCA | 1 | 2925 | |
| | | | AIOps Log Fault Diagnosis | LFD | 1 | 3000 | |
| | | **Coding** | Text-to-SQL | SPIDER | 1 | 2147 | |
| | | | Knowledge-Augmented Text-to-SQL | BIRD | 1 | 1534 | |
| | **Multi-turn, Simulated** | **Embodied** | Household Embodied Decision Making | ALFWorld | 30 | 2000 | |
| | | | Scientific Embodied Decision Making | ScienceWorld | 10-30 | 1857 | |
| | **Multi-turn, Real-world** | **Information Seeking** | Web-based Deep Search | 2Wiki | 5 | 2500 | |
| | | **Medical** | Complex Tabular Reasoning on Electronic Health Records | MIMIC-III | 5 | 2500 | |
|
|
| ## Data Format |
|
|
| Each config can be loaded independently from Hugging Face, and each task keeps the fields needed by its original environment. All tasks include a `task` field, which is the main query or observation presented to the agent. |
|
|
|
|
| ## Load the Dataset |
|
|
| Using `datasets`: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load one task |
| ddxplus = load_dataset("guosy/DTLBench", "ddxplus") |
| print(ddxplus["train"][0]) |
| |
| # Load another task |
| spider = load_dataset("guosy/DTLBench", "spider") |
| print(spider["train"][0]["task"]) |
| ``` |
|
|
| Using `huggingface-cli`: |
|
|
| ```bash |
| huggingface-cli download --repo-type dataset guosy/DTLBench --local-dir ./DTLBench |
| ``` |
|
|
| ## License |
|
|
| DTLBench is a **mixed-license** collection. Each subdataset follows its own original license, and the benchmark authors do not claim additional rights beyond those licenses. |
|
|
| Please see [LICENSE.md](LICENSE.md) and the per-task `LICENSE` files for details. In particular: |
|
|
| - Some tasks are under permissive licenses such as MIT or Apache-2.0 |
| - Some tasks use CC licenses with attribution or share-alike requirements |
| - Some tasks have unclear or unknown redistribution terms |
|
|
| You are responsible for ensuring your use complies with the license of each individual subdataset. |
|
|
| ## Citation |
|
|
| If you use DTLBench, please consider citing our paper: |
| ``` |
| @misc{guo2026cascadecasebasedcontinualadaptation, |
| title={CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment}, |
| author={Siyuan Guo and Yali Du and Hechang Chen and Yi Chang and Jun Wang}, |
| year={2026}, |
| eprint={2605.06702}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.AI}, |
| url={https://arxiv.org/abs/2605.06702}, |
| } |
| ``` |
