GraphCanon updated Sep 9, 2026 · GitHub synced Sep 9, 2026
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Decision brief
Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024.
Good fit when
- If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights.
- When your project involves learning from human demonstrations to teach LLMs how to ground abstract concepts into physical actions, such as robot manipulation tasks.
Avoid when
- If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024.
- For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (887d since push)
- As of Sep 9, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 9, 2026
- Security (OSV)
- No lockfile
- As of Jul 15, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/azminewasi/Awesome-LLMs-ICLR-24Similar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A resource hub for Large Language Models research accepted at the International Conference on Learning Representations (ICLR) in 2024.
Capability facts
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README
Grounding Language Plans in Demonstrations Through Counter Factual Perturbations This paper explores the potential of Large Language Models (LLMs) to improve robot manipulation by leveraging concepts from plan ning literature. Specifically, the authors introduce a framework that...
For agents
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