Alternatives and similar repositories for InfoBench

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

• thu-coai / ComplexBench
  Benchmarking Complex Instruction-Following with Multiple Constraints Composition (NeurIPS 2024 Datasets and Benchmarks Track)
  ☆95Updated 8 months ago
• YJiangcm / FollowBench
  [ACL 2024] FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
  ☆115Updated 4 months ago
• OpenLMLab / LongWanjuan
  Towards Systematic Measurement for Long Text Quality
  ☆36Updated last year
• HillZhang1999 / ICD
  Code & Data for our Paper "Alleviating Hallucinations of Large Language Models through Induced Hallucinations"
  ☆69Updated last year
• DAMO-NLP-SG / TempReason
  ☆33Updated last year
• qtli / GSM-Plus
  GSM-Plus: Data, Code, and Evaluation for Enhancing Robust Mathematical Reasoning in Math Word Problems.
  ☆63Updated last year
• October2001 / ProLong
  [ACL 2024 (Oral)] A Prospector of Long-Dependency Data for Large Language Models
  ☆57Updated last year
• 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…
  ☆121Updated last year
• GAIR-NLP / alignment-for-honesty
  ☆75Updated last year
• princeton-nlp / LLMBar
  [ICLR 2024] Evaluating Large Language Models at Evaluating Instruction Following
  ☆132Updated last year
• hkust-nlp / felm
  Github repository for "FELM: Benchmarking Factuality Evaluation of Large Language Models" (NeurIPS 2023)
  ☆59Updated last year
• GAIR-NLP / BeHonest
  BeHonest: Benchmarking Honesty in Large Language Models
  ☆34Updated last year
• SparkJiao / dpo-trajectory-reasoning
  [EMNLP 2024] Source code for the paper "Learning Planning-based Reasoning with Trajectory Collection and Process Rewards Synthesizing".
  ☆82Updated 9 months ago
• zhu-minjun / PAlign
  Personality Alignment of Language Models
  ☆47Updated 3 months ago
• OpenMOSS / Say-I-Dont-Know
  [ICML'2024] Can AI Assistants Know What They Don't Know?
  ☆83Updated last year
• swj0419 / in-context-pretraining
  ☆54Updated last year
• Spico197 / MoE-SFT
  🍼 Official implementation of Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts
  ☆41Updated last year
• icip-cas / awesome-auto-alignment
  Collection of papers for scalable automated alignment.
  ☆94Updated last year
• princeton-nlp / QuRating
  [ICML 2024] Selecting High-Quality Data for Training Language Models
  ☆189Updated last year
• nayeon7lee / FactualityPrompt
  ☆86Updated 2 years ago
• meowpass / FollowComplexInstruction
  Official implementation of the paper "From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large L…
  ☆51Updated last year
• chujiezheng / LLM-Extrapolation
  Official repository for ACL 2025 paper "Model Extrapolation Expedites Alignment"
  ☆75Updated 5 months ago
• yinzhangyue / SelfAware
  Do Large Language Models Know What They Don’t Know?
  ☆99Updated 11 months ago
• AI21Labs / factor
  Code and data for the FACTOR paper
  ☆52Updated last year
• Spico197 / awesome-lm-evaluation
  🩺 A collection of ChatGPT evaluation reports on various bechmarks.
  ☆50Updated 2 years ago
• Abbey4799 / CELLO
  Code and data for the paper "Can Large Language Models Understand Real-World Complex Instructions?"(AAAI2024)
  ☆49Updated last year
• GAIR-NLP / ReasonEval
  [AAAI 2025 oral] Evaluating Mathematical Reasoning Beyond Accuracy
  ☆73Updated 2 weeks ago
• ChiyuSONG / dynamics-of-instruction-tuning
  ☆17Updated 7 months ago
• FreedomIntelligence / OVM
  ☆69Updated last year
• siyuyuan / coscript
  Resources for our ACL 2023 paper: Distilling Script Knowledge from Large Language Models for Constrained Language Planning
  ☆36Updated 2 years ago