Alternatives and similar repositories for FactCHD

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

• hyintell / awesome-refreshing-llms
  EMNLP'23 survey: a curation of awesome papers and resources on refreshing large language models (LLMs) without expensive retraining.
  ☆136Updated last year
• yuxiaw / Factcheck-GPT
  Fact-Checking the Output of Generative Large Language Models in both Annotation and Evaluation.
  ☆108Updated last year
• yueyu1030 / AttrPrompt
  [NeurIPS 2023] This is the code for the paper `Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias`.
  ☆156Updated 2 years 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…
  ☆125Updated last year
• oriyor / reasoning-on-cots
  Implementation of the paper: "Answering Questions by Meta-Reasoning over Multiple Chains of Thought"
  ☆96Updated last year
• WadeYin9712 / Dynosaur
  Code and data for "Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation" (EMNLP 2023)
  ☆64Updated 2 years ago
• TIGER-AI-Lab / MAmmoTH2
  Official code for "MAmmoTH2: Scaling Instructions from the Web" [NeurIPS 2024]
  ☆149Updated last year
• zorazrw / filco
  [Preprint] Learning to Filter Context for Retrieval-Augmented Generaton
  ☆197Updated last year
• Leezekun / Directional-Stimulus-Prompting
  [NeurIPS 2023] Codebase for the paper: "Guiding Large Language Models with Directional Stimulus Prompting"
  ☆111Updated 2 years 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"
  ☆78Updated last year
• abhika-m / FAVA
  ☆75Updated last year
• princeton-nlp / LLMBar
  [ICLR 2024] Evaluating Large Language Models at Evaluating Instruction Following
  ☆134Updated last year
• i-Eval / FairEval
  ☆142Updated 2 years ago
• ConiferLM / Conifer
  Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models
  ☆89Updated last year
• AI21Labs / in-context-ralm
  ☆294Updated last year
• yinzhangyue / SelfAware
  Do Large Language Models Know What They Don’t Know?
  ☆102Updated last year
• Hannibal046 / SelfMemory
  [Neurips2023] Source code for Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
  ☆62Updated 2 years ago
• oriyor / ret-robust
  Implementation of the paper: "Making Retrieval-Augmented Language Models Robust to Irrelevant Context"
  ☆75Updated last year
• lifan-yuan / CRAFT
  Code for ICLR 2024 paper "CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets"
  ☆60Updated last year
• anthonywchen / RARR
  RARR: Researching and Revising What Language Models Say, Using Language Models
  ☆49Updated 2 years ago
• RUCAIBox / LLM-Knowledge-Boundary
  Implementation of "Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation"
  ☆82Updated 2 years ago
• qhjqhj00 / WebBrain
  ☆68Updated 2 years ago
• HKUNLP / icl-ceil
  [ICML 2023] Code for our paper “Compositional Exemplars for In-context Learning”.
  ☆103Updated 2 years ago
• AlexTMallen / adaptive-retrieval
  ☆189Updated 5 months ago
• kongds / scaling_sentemb
  Scaling Sentence Embeddings with Large Language Models
  ☆109Updated last year
• xsc1234 / Search-in-the-Chain
  Code for Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks (WWW 2024))
  ☆58Updated 3 weeks ago
• gankim / tree-of-clarifications
  🌲 Code for our EMNLP 2023 paper - 🎄 "Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Mode…
  ☆52Updated 2 years ago
• chaitanyamalaviya / ExpertQA
  [Data + code] ExpertQA : Expert-Curated Questions and Attributed Answers
  ☆135Updated last year
• RuochenZhao / Verify-and-Edit
  A framework for editing the CoTs for better factuality
  ☆50Updated last year
• Junjie-Ye / ToolEyes
  [COLING 2025] ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios
  ☆71Updated 6 months ago