Alternatives and similar repositories for LLMBar:

Users that are interested in LLMBar are comparing it to the libraries listed below

• chujiezheng / LLM-Extrapolation
  Official repository for paper "Weak-to-Strong Extrapolation Expedites Alignment"
  ☆74Updated 10 months ago
• QwenLM / ProcessBench
  ☆150Updated 4 months ago
• shizhediao / R-Tuning
  [NAACL 2024 Outstanding Paper] Source code for the NAACL 2024 paper entitled "R-Tuning: Instructing Large Language Models to Say 'I Don't…
  ☆110Updated 9 months ago
• GAIR-NLP / alignment-for-honesty
  ☆73Updated 11 months ago
• SparkJiao / dpo-trajectory-reasoning
  [EMNLP 2024] Source code for the paper "Learning Planning-based Reasoning with Trajectory Collection and Process Rewards Synthesizing".
  ☆76Updated 3 months ago
• OSU-NLP-Group / LLM-Knowledge-Conflict
  [ICLR'24 Spotlight] "Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts"
  ☆67Updated last year
• GAIR-NLP / ReasonEval
  [AAAI 2025 oral] Evaluating Mathematical Reasoning Beyond Accuracy
  ☆60Updated 4 months ago
• FranxYao / FlanT5-CoT-Specialization
  Implementation of ICML 23 Paper: Specializing Smaller Language Models towards Multi-Step Reasoning.
  ☆130Updated last year
• yinzhangyue / SelfAware
  Do Large Language Models Know What They Don’t Know?
  ☆94Updated 5 months ago
• Junjie-Ye / ToolEyes
  [COLING 2025] ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios
  ☆65Updated 5 months ago
• xingyaoww / mint-bench
  Official Repo for ICLR 2024 paper MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback by Xingyao Wang*, Ziha…
  ☆123Updated 11 months ago
• icip-cas / awesome-auto-alignment
  Collection of papers for scalable automated alignment.
  ☆89Updated 6 months ago
• TIGER-AI-Lab / MAmmoTH2
  Official code for "MAmmoTH2: Scaling Instructions from the Web" [NeurIPS 2024]
  ☆139Updated 6 months ago
• IBM / SALMON
  Self-Alignment with Principle-Following Reward Models
  ☆160Updated last year
• OSU-NLP-Group / llm-planning-eval
  [ACL'24] Code and data of paper "When is Tree Search Useful for LLM Planning? It Depends on the Discriminator"
  ☆54Updated last year
• thu-coai / ComplexBench
  Benchmarking Complex Instruction-Following with Multiple Constraints Composition (NeurIPS 2024 Datasets and Benchmarks Track)
  ☆81Updated 2 months ago
• princeton-nlp / MQuAKE
  [EMNLP 2023] MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
  ☆109Updated 7 months ago
• mtbench101 / mt-bench-101
  [ACL 2024] MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues
  ☆85Updated 9 months ago
• hkust-nlp / llm-compression-intelligence
  Official github repo for the paper "Compression Represents Intelligence Linearly" [COLM 2024]
  ☆133Updated 7 months ago
• qtli / GSM-Plus
  GSM-Plus: Data, Code, and Evaluation for Enhancing Robust Mathematical Reasoning in Math Word Problems.
  ☆60Updated 9 months ago
• GAIR-NLP / weak-to-strong-reasoning
  ☆59Updated 8 months ago
• GAIR-NLP / OPO
  ☆49Updated last year
• qinyiwei / InfoBench
  ☆53Updated 8 months ago
• WeiminXiong / IPR
  Watch Every Step! LLM Agent Learning via Iterative Step-level Process Refinement (EMNLP 2024 Main Conference)
  ☆57Updated 6 months ago
• hkust-nlp / felm
  Github repository for "FELM: Benchmarking Factuality Evaluation of Large Language Models" (NeurIPS 2023)
  ☆59Updated last year
• princeton-nlp / QuRating
  [ICML 2024] Selecting High-Quality Data for Training Language Models
  ☆169Updated 10 months ago
• GAIR-NLP / ReAlign
  Reformatted Alignment
  ☆115Updated 7 months ago
• SuperBruceJia / Awesome-LLM-Self-Consistency
  Awesome LLM Self-Consistency: a curated list of Self-consistency in Large Language Models
  ☆96Updated 8 months ago
• NingMiao / SelfCheck
  Code for the paper <SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning>
  ☆49Updated last year
• JoeYing1019 / UltraTool
  [ACL2024] Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios
  ☆56Updated last year