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Found 8 papers, 5 papers with code
SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning
Evaluating large language models' (LLMs) long-context understanding capabilities remains challenging.
LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch
We detail the training of the LLM360 K2-65B model, scaling up our 360-degree OPEN SOURCE approach to the largest and most powerful models under project LLM360.
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability
To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety.
Explore the Reasoning Capability of LLMs in the Chess Testbed
Based on the observation that expert chess players employ a dual approach combining long-term strategic play with short-term tactical play along with language explanation, we propose improving the reasoning capability of large language models in chess by integrating annotated strategy and tactic.
ToolGen: Unified Tool Retrieval and Calling via Generation
As large language models (LLMs) advance, their inability to autonomously execute tasks by directly interacting with external tools remains a critical limitation.
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed.
Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents
However, LLMs are optimized for language generation instead of tool use during training or alignment, limiting their effectiveness as agents.
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks.
