| Date |
Session |
Materials |
| 17.4. |
Orga |
slides |
| 24.4. |
Paper 1: “TALM: Tool Augmented Language Models”, Parisi et al., 2022 |
reading group |
| 1.5. |
Feiertag |
|
| 8.5. |
Paper 2: “Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension”, Andor et al., 2019
Paper 3: “Training Verifiers to Solve Math Word Problems”, Cobbe et al., 2021 |
reading group |
| 15.5. |
Kim: “PAL: Program-aided Language Models”, Gao et al., 2022
Raphael: “Toolformer: Language Models Can Teach Themselves to Use Tools”, Schick et al., 2023 |
|
| 22.5. |
Davit: “Making Language Models Better Tool Learners with Execution Feedback”, Qiao et al., 2023
Kim: “Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models”, Hsieh et al., 2023 |
|
| 29.5. |
Davit: “RestGPT: Connecting Large Language Models with Real-World RESTful APIs”, Song et al., 2023
Paper 9: “Syntax Error-Free and Generalizable Tool Use for LLMs via Finite-State Decoding”, Zhang et al., 2023 |
|
| 5.6. |
Paper 10: “ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings”, Hao et al., 2023
Myta: “Large Language Models as Tool Makers”, Cai et al., 2023 |
|
| 12.6. |
Paper 12: “Gorilla: Large Language Model Connected with Massive APIs”, Patil et al., 2023
Nikitas: “ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs”, Qin et al., 2023 |
|
| 19.6. |
David: “API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs”, Li et al., 2023
Tim: “OpenAGI: When LLM Meets Domain Experts”, Ge et al., 2023 |
|
| 26.6. |
David: “Voyager: An Open-Ended Embodied Agent with Large Language Models”, Wang et al., 2023 |
|
| 3.7. |
Backup |
|
| 10.7. |
Backup |
|
| 17.7. |
Implementation Project |
|
| 24.7. |
Implementation Project |
|
