Develop Benchmarking Framework for T2S Agent on Article Relevance and Summarization Accuracy

[ansh-info]

Is your feature request related to a problem? Please describe.

While the T2S agent aims to assist users in identifying pertinent scientific articles and generating accurate summaries and answers, there currently lacks a systematic approach to evaluate its effectiveness in these tasks. Specifically, there's a need to assess its performance in real-world scenarios, such as those outlined in Use Cases 1 and 2, to ensure reliability and accuracy.

Describe the solution you'd like

We propose the development of a comprehensive benchmarking framework that includes:

1. Benchmark Dataset Creation:

   • Curate a dataset comprising specific topics (e.g., "TNFα inhibition in Crohn’s disease") with a collection of relevant and irrelevant articles.
   • Include human-annotated summaries and Q&A pairs for these articles to serve as ground truth.

2. Evaluation Metrics:

   • Define clear metrics to assess:

     • Article relevance identification (e.g., precision, recall, F1-score).
     • Summarization accuracy (e.g., ROUGE, BLEU scores).
     • Q&A correctness (e.g., exact match, semantic similarity).

3. Benchmarking Pipeline:

   • Implement an automated pipeline to run the T2S agent on the benchmark dataset and compute the defined metrics.
   • Facilitate regular evaluations to monitor improvements over time.

4. Reporting and Analysis:

   • Generate detailed reports highlighting areas of strength and opportunities for enhancement in the T2S agent's performance.

Describe alternatives you've considered

• Manual evaluation of the T2S agent's outputs, which is time-consuming and may lack consistency.
• Utilizing existing general-purpose QA benchmarks, which may not adequately reflect the specific requirements of life science literature analysis.

Additional context

Implementing this benchmarking framework will provide valuable insights into the T2S agent's capabilities and guide future development efforts to enhance its utility in life science research contexts.