GAIR-NLP / MoPSLinks
[ACL 2024] Code for "MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation"
☆39Updated last year
Alternatives and similar repositories for MoPS
Users that are interested in MoPS are comparing it to the libraries listed below
Sorting:
- ☆58Updated last year
- Official repository for ACL 2025 paper "Model Extrapolation Expedites Alignment"☆75Updated 4 months ago
- [AAAI 2025 oral] Evaluating Mathematical Reasoning Beyond Accuracy☆69Updated 9 months ago
- BeHonest: Benchmarking Honesty in Large Language Models☆34Updated last year
- GSM-Plus: Data, Code, and Evaluation for Enhancing Robust Mathematical Reasoning in Math Word Problems.☆63Updated last year
- ☆75Updated last year
- ☆30Updated 9 months ago
- [ACL 2024 Findings] CriticBench: Benchmarking LLMs for Critique-Correct Reasoning☆27Updated last year
- Suri: Multi-constraint instruction following for long-form text generation (EMNLP’24)☆25Updated this week
- Evaluate the Quality of Critique☆36Updated last year
- [EMNLP 2024] Source code for the paper "Learning Planning-based Reasoning with Trajectory Collection and Process Rewards Synthesizing".☆82Updated 8 months ago
- ☆44Updated last year
- [ICLR'25] Data and code for our paper "Why Does the Effective Context Length of LLMs Fall Short?"☆77Updated 10 months ago
- [ICML'2024] Can AI Assistants Know What They Don't Know?☆83Updated last year
- ☆69Updated last year
- [ICML'24] TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks☆30Updated last year
- [ICLR'24 Spotlight] "Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts"☆77Updated last year
- Source code of "Reasons to Reject? Aligning Language Models with Judgments"☆58Updated last year
- [ICLR 2024] Evaluating Large Language Models at Evaluating Instruction Following☆131Updated last year
- ☆14Updated last year
- [ACL 2024 (Oral)] A Prospector of Long-Dependency Data for Large Language Models☆56Updated last year
- 🍼 Official implementation of Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts☆40Updated last year
- ☆56Updated last year
- Github repository for "FELM: Benchmarking Factuality Evaluation of Large Language Models" (NeurIPS 2023)☆59Updated last year
- [NAACL 2024 Outstanding Paper] Source code for the NAACL 2024 paper entitled "R-Tuning: Instructing Large Language Models to Say 'I Don't…☆121Updated last year
- The official repository of the Omni-MATH benchmark.☆88Updated 9 months ago
- ☆13Updated last year
- Code & Data for our Paper "Alleviating Hallucinations of Large Language Models through Induced Hallucinations"☆69Updated last year
- Official code for "MAmmoTH2: Scaling Instructions from the Web" [NeurIPS 2024]☆148Updated 11 months ago
- [ICLR'24 spotlight] Tool-Augmented Reward Modeling☆51Updated 4 months ago