Benchmarks - #7
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…towards benchmarking LLMs in reasoning; enhance .gitignore for local data
Bringing it to par to main
… to formally benchmark model performance on logical puzzles, game theory, and constrained decision-making tasks
…tputs - Added `exporter.py` to handle the serialization of DebateState into structured JSON and human-readable Markdown reports. - Introduced `faiss_index.py` for FAISS-backed vector indexing and retrieval. - Created `models.py` to define data models for arguments, claims, and evaluation scores. - Developed `prompts.py` to manage prompt templates for argument generation and retrieval. - Implemented `reasonbench_eval.py` for evaluating reasoning tasks with structured outputs. - Added `reasonbench_models.py` to define models for ReasonBench evaluation tasks. - Established `retriever.py` to abstract the vector index and facilitate evidence retrieval. - Defined `state.py` to manage the DebateState structure and its associated operations.
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Bringing all changes to main