IIT Kharagpur's Innovative Long-Context LLM Shines in Narrative Consistency
Analysis
This project from IIT Kharagpur presents a compelling approach to evaluating long-context reasoning in LLMs, focusing on causal and logical consistency within a full-length novel. The team's use of a fully local, open-source setup is particularly noteworthy, showcasing accessible innovation in AI research. It's fantastic to see advancements in understanding narrative coherence at such a scale!
Key Takeaways
- •The project utilizes a fully local, open-source approach with Pathway for document ingestion and Ollama (Llama 2.5, 7B) for local LLM inference.
- •The research focuses on assessing causal and logical consistency between character backstories and entire novels (100k+ words).
- •It demonstrates the potential of constraint tracking and evidence-based decision-making in long-context reasoning within LLMs.
Reference
“The goal was to evaluate whether large language models can determine causal and logical consistency between a proposed character backstory and an entire novel (~100k words), rather than relying on local plausibility.”