Skip to content

Latest commit

 

History

History
99 lines (91 loc) · 6.83 KB

ChatQA_Building_GPT_4_Level_Conversational_QA_Models.md

File metadata and controls

99 lines (91 loc) · 6.83 KB
Raw

SUMMARY

The paper introduces CH AQ q a7b, a conversational QA model matching GP4's accuracy through a two-step instruction tuning process, improved retriever, and meticulous data curation.

IDEAS:

  • Conversational QA models allow users to interact in a conversational manner.
  • These models generate answers without needing specific fine-tuning for each data set.
  • They can incorporate retrieved information in both open domain or long document settings.
  • Creating a conversational QA model matching top-tier models like GP4 is challenging.
  • CH AQ q a7b matches GP4's accuracy through a two-step instruction tuning process.
  • The model uses an improved retriever for retrieval augmented generation in conversational QA.
  • A meticulous data curation process enhances the language model's ability to integrate context.
  • The method outperforms regular instruction tuning or RHF-based methods for retrieval augmented generation.
  • Fine-tuning state-of-the-art single-turn query retrievers on multi-turn QA data is effective.
  • The chat QA 70b model outperforms GPT 3.5 turbo and GPT 4 in average score without synthetic data.
  • Adding unanswerable samples in instruction tuning reduces hallucination in conversational QA.
  • Instruction tuning has become key in building state-of-the-art dialogue agents.
  • Context enhanced instruction tuning improves the model's ability to handle context-aware QA.
  • Synthetic data generation using GPT 3.5 turbo creates high-quality data sets for fine-tuning.
  • Fine-tuning a single-turn retriever using conversational query and context pairs is effective.
  • Query rewriting methods are compared to fine-tuning methods for conversational retrieval.
  • Fine-tuning performs better than query rewriting on the E5 unsupervised model.
  • Using human annotated data leads to significant improvements on certain data sets.
  • Human evaluations confirm the strong performance of the model compared to GPT 4.
  • Incorporating retrieved top K chunks during training enhances performance on retrieval-based data sets.
  • More contexts don't always lead to better results; top five contexts are optimal.
  • The order of context chunks affects results; sequential order is effective.
  • Fine-tuned retrievers improve average scores compared to non-fine-tuned versions.
  • Models more accurate with unanswerable samples tend to be less accurate with answerable samples.
  • Adding unanswerable samples improves overall conversational QA score.

INSIGHTS:

  • Conversational QA models enhance user experience by allowing follow-up questions and clarifications.
  • Instruction tuning is crucial for building effective dialogue agents without prior training.
  • Context enhanced instruction tuning significantly improves context-aware QA capabilities.
  • Synthetic data generation can create high-quality data sets for fine-tuning language models.
  • Fine-tuning single-turn retrievers on multi-turn data is as effective as using top-tier models.
  • Human annotated data improves performance on unanswerable cases, enhancing overall accuracy.
  • Incorporating retrieved top K chunks during training balances context handling in long documents.
  • Optimal context chunk order and number are critical for effective answer extraction.
  • Fine-tuned retrievers significantly enhance question answering performance over non-fine-tuned versions.
  • Balancing answerable and unanswerable sample accuracy is key for robust QA models.

QUOTES:

  • "Conversational QA models allow users to interact in a conversational manner."
  • "These models generate answers without needing specific fine-tuning for each data set."
  • "Creating a conversational QA model matching top-tier models like GP4 is challenging."
  • "CH AQ q a7b matches GP4's accuracy through a two-step instruction tuning process."
  • "The method outperforms regular instruction tuning or RHF-based methods for retrieval augmented generation."
  • "Fine-tuning state-of-the-art single-turn query retrievers on multi-turn QA data is effective."
  • "The chat QA 70b model outperforms GPT 3.5 turbo and GPT 4 in average score without synthetic data."
  • "Adding unanswerable samples in instruction tuning reduces hallucination in conversational QA."
  • "Instruction tuning has become key in building state-of-the-art dialogue agents."
  • "Context enhanced instruction tuning improves the model's ability to handle context-aware QA."
  • "Synthetic data generation using GPT 3.5 turbo creates high-quality data sets for fine-tuning."
  • "Fine-tuning a single-turn retriever using conversational query and context pairs is effective."
  • "Query rewriting methods are compared to fine-tuning methods for conversational retrieval."
  • "Fine-tuning performs better than query rewriting on the E5 unsupervised model."
  • "Using human annotated data leads to significant improvements on certain data sets."
  • "Human evaluations confirm the strong performance of the model compared to GPT 4."
  • "Incorporating retrieved top K chunks during training enhances performance on retrieval-based data sets."
  • "More contexts don't always lead to better results; top five contexts are optimal."
  • "The order of context chunks affects results; sequential order is effective."
  • "Fine-tuned retrievers improve average scores compared to non-fine-tuned versions."

HABITS:

  • Regularly fine-tune single-turn query retrievers on multi-turn QA data sets.
  • Incorporate unanswerable samples in instruction tuning to reduce hallucination.
  • Use synthetic data generation tools like GPT 3.5 turbo for creating high-quality data sets.
  • Conduct human evaluations to assess the accuracy of generated answers.
  • Experiment with different context chunk orders and numbers for optimal results.

FACTS:

  • Conversational QA models can generate answers without specific fine-tuning for each data set.
  • CH AQ q a7b matches GP4's accuracy through a two-step instruction tuning process.
  • The chat QA 70b model outperforms GPT 3.5 turbo and GPT 4 in average score without synthetic data.
  • Adding unanswerable samples in instruction tuning reduces hallucination in conversational QA.
  • Instruction tuning has become key in building state-of-the-art dialogue agents.

REFERENCES:

  • CH AQ q a7b
  • GPT 3.5 turbo
  • GPT 4
  • Dragon retriever
  • E5 unsupervised model
  • CoQA
  • DQA
  • ConVFQA
  • SQA
  • Hybrid Dial

ONE-SENTENCE TAKEAWAY

CH AQ q a7b achieves GP4-level accuracy through innovative two-step instruction tuning and improved retriever methods.

RECOMMENDATIONS:

  • Use conversational QA models for interactive user experiences with follow-up questions and clarifications.
  • Implement two-step instruction tuning for building effective dialogue agents without prior training.
  • Generate synthetic data sets using tools like GPT 3.5 turbo for high-quality fine-tuning material.
  • Fine-tune single-turn retrievers on multi-turn QA data for effective retrieval augmented generation.
  • Incorporate unanswerable samples in instruction tuning to reduce hallucination and improve accuracy.